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Kurunthogai is a classical Tamil poetic masterpiece and it is the second book of Ettuthokai which is one of the Sangam literary works. The poems of Kurunthogai expresses the love life between men and women who lived during the Sangam age. Kurunthogai is a massive work written by many authors. The poems are written based on the five different landscapes namely, Kurinchi, Mullai, Marutham, Neythal, and Pālai. So, the poems contain much valuable historical information related to these landscapes. This paper proposes a template-based Information Extraction (IE) framework for Kurunthogai which automatically extracts the names of flora, fauna, foods, vessels, and water bodies described in it. Furthermore, it extracts Noun Unigrams, Verb Unigrams, Adjective-Noun Bigrams, and Adverb-Verb Bigrams. Tamil Morphological Analyzer tool has been used to extract the N-grams. The state-of-art IE techniques have attempted to extract information from expository texts, whereas, the proposed IE framework extracts information from a literature-based text. The existing techniques extract information from monolingual texts, whereas, the proposed IE framework extracts information from bilingual texts. The proposed IE framework has achieved a precision of 88.8%. The proposed framework can be applied for any literature type of texts and be used in various applications of Natural Language Processing. [ABSTRACT FROM AUTHOR]

Motivation: While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not familiar with recent advances in NLP. This article provides a comparative evaluation of several state-of-the-art natural language parsers, focusing on the task of extracting protein–protein interaction (PPI) from biomedical papers. We measure how each parser, and its output representation, contributes to accuracy improvement when the parser is used as a component in a PPI system. Results: All the parsers attained improvements in accuracy of PPI extraction. The levels of accuracy obtained with these different parsers vary slightly, while differences in parsing speed are larger. The best accuracy in this work was obtained when we combined Miyao and Tsujiis Enju parser and Charniak and Johnsons reranking parser, and the accuracy is better than the state-of-the-art results on the same data. Availability: The PPI extraction system used in this work (AkanePPI) is available online at http://www-tsujii.is.s.u-tokyo.ac.jp/-100downloads/downloads.cgi. The evaluated parsers are also available online from each developers site. Contact: yusuke@is.s.u-tokyo.ac.jp [ABSTRACT FROM AUTHOR]

Feature engineering is one of the most complex aspects of system design in machine learning. Fortunately, kernel methods provide the designer with formidable tools to tackle such complexity. Among others, tree kernels (TKs) have been successfully applied for representing structured data in diverse domains, ranging from bioinformatics and data mining to natural language processing. One drawback of such methods is that learning with them typically requires a large number of kernel computations (quadratic in the number of training examples) between training examples. However, in practice substructures often repeat in the data which makes it possible to avoid a large number of redundant kernel evaluations. In this paper, we propose the use of Directed Acyclic Graphs (DAGs) to compactly represent trees in the training algorithm of Support Vector Machines. In particular, we use DAGs for each iteration of the cutting plane algorithm (CPA) to encode the model composed by a set of trees. This enables DAG kernels to efficiently evaluate TKs between the current model and a given training tree. Consequently, the amount of total computation is reduced by avoiding redundant evaluations over shared substructures. We provide theory and algorithms to formally characterize the above idea, which we tested on several datasets. The empirical results confirm the benefits of the approach in terms of significant speedups over previous state-of-the-art methods. In addition, we propose an alternative sampling strategy within the CPA to address the class-imbalance problem, which coupled with fast learning methods provides a viable TK learning framework for a large class of real-world applications. [ABSTRACT FROM AUTHOR]

Crossword puzzles are used everyday by millions of people for entertainment, but have applications also in educational and rehabilitation contexts. Unfortunately, the generation of ad-hoc puzzles, especially on specific subjects, typically requires a great deal of human expert work. This paper presents the architecture of WebCrow-generation, a system that is able to generate crosswords with no human intervention, including clue generation and crossword compilation. In particular, the proposed system crawls information sources on the Web, extracts definitions from the downloaded pages using state-of-the-art natural language processing techniques and, finally, compiles the crossword schema with the extracted definitions by constraint satisfaction programming. The system has been tested on the creation of Italian crosswords, but the extensive use of machine learning makes the system easily portable to other languages. [ABSTRACT FROM AUTHOR]

Legal reasoning and judicial verdicts in many legal systems are highly dependent on case law. The ever increasing number of case law make the task of comprehending case law in a legal case cumbersome for legal practitioners; and this invariably stifles their efficiency. Legal reasoning and judicial verdicts will therefore be easier and faster, if case law were in abridged form that preserves their original meaning. This paper used the General Information Extraction System Architecture approach and integrated Natural Language Processing, Annotation, and Information Extraction tools to develop a software system that does automatic extractive text summarisation of Nigeria Supreme Court case law. The summarised case law which were about 20% of their original, were evaluated for semantic preservation and has shown to be 83% reliable. [ABSTRACT FROM AUTHOR]

Ontologies are one of the fundamental elements of the Semantic Web, and they have gained a lot of popularity and recognition because they are viewed as the answer to the need for interoperable semantics in modern information systems. The intermingling of techniques in areas such as natural language processing, information retrieval, machine learning, data mining, and knowledge representation provide a lot of possibilities for development of ontology learning approaches. A rise in focus on the ability to cope with the scale of Web data required for ontology learning forces the potential growth of cross-language research, emphasizing the automatic or semi-automatic generation of the tools dedicated to text mining and information extraction. This paper presents the integration of ontology learning tools from text in the knowledge repository to incorporate the applied techniques and outputs of an ontology learning algorithm into the one complex multifunctional solution. The proposed knowledge repository covers various applicability of existing techniques of learning ontologies from text, and offers competency question-based reasoning mechanism for individuals to specify their profiles of ontology learning tools. The validation stage is also provided in the form of applied reasoning. [ABSTRACT FROM AUTHOR]

The paper presents an Object Oriented Analysis and Design (OOAD) approach to modeling, reasoning and a database query based approach to processing and solving addition-subtraction (Add-Sub) type arithmetic Mathematical Word Problems (MWP) of elementary school level. The system identifies and extracts the key entities in a word problem like owners, items and their attributes and quantities, verbs, from all the input sentences, using a rule based Information Extraction (IE) approach based on Semantic Role Labeling (SRL) technique. These information are then stored in predefined templates which are further modeled to represent an MWP in the object-oriented paradigm and processed using query based approach to generate the answer. These kind of applications are based on Natural Language Processing (NLP), Natural Language Understanding (NLU) and Artificial Intelligence (AI), and can be used as intelligent dynamic mathematical tutoring tools as part of E-Learning systems, Learning Management Systems, on-line education, etc. The proposed object oriented mathematical word problem solver can solve arithmetic MWPs involving only addition-subtraction operations and it has produced an accuracy of 94.35% on a subset of the AI2 arithmetic questions dataset. [ABSTRACT FROM AUTHOR]

In the fast growing of digital technologies, crawlers and search engines face unpredictable challenges. Focused web-crawlers are essential for mining the boundless data available on the internet. Web-Crawlers face indeterminate latency problem due to differences in their response time. The proposed work attempts to optimize the designing and implementation of Focused Web-Crawlers using Master-Slave architecture for Bioinformatics web sources. Focused Crawlers ideally should crawl only relevant pages, but the relevance of the page can only be estimated after crawling the genomics pages. A solution for predicting the page relevance, which is based on Natural Language Processing, is proposed in the paper. The frequency of the keywords on the top ranked sentences of the page determines the relevance of the pages within genomics sources. The proposed solution uses a TextRank algorithm to rank the sentences, as well as ensuring the correct classification of Bioinformatics web page. Finally, the model is validated by being compared with a breadth first search web-crawler. The comparison shows significant reduction in run time for the same harvest rate. [ABSTRACT FROM AUTHOR]

Abstract Social media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20 and 29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset. • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries. • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets. • Novel application of word embeddings in flooding context identified relevant tweets. • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim). [ABSTRACT FROM AUTHOR]

Abstract Fuzzy rule-based systems are effective tools for acquiring knowledge from data and represent it in a linguistically interpretable form. To achieve interpretability, input features are granulated in fuzzy partitions. A critical design decision is the selection of the granularity level for each input feature. This paper presents an approach, called DC* (Double Clustering with A*), for automatically designing interpretable fuzzy partitions with optimal granularity. DC* is specific for classification problems and is mainly based on a two-stage process: the first stage identifies clusters of multidimensional samples in order to derive class-labeled prototypes; in the second stage the one-dimensional projections of such prototypes are further clustered along each dimension simultaneously, thus minimizing the number of clusters for each feature. Moreover, the resulting one-dimensional clusters provide information to define fuzzy partitions that satisfy a number of interpretability constraints and exhibit variable granularity levels. The fuzzy sets in each partition can be labeled by meaningful linguistic terms and used to represent knowledge in a natural language form. Experimental results on both synthetic and real data show that the derived fuzzy partitions can be exploited to define very compact fuzzy rule-based systems that exhibit high linguistic interpretability and good classification accuracy. Highlights • Fuzzy systems get knowledge from data, granulating input features in fuzzy partitions. • DC*: our proposal to designs interpretable fuzzy partitions with optimal granularity. • DC* performs a 2-stage clustering process and is specific for classification problems. • The number of clusters is minimized through a mechanism relying on the A* algorithm. • Resulting fuzzy systems show linguistic interpretability and classification accuracy. [ABSTRACT FROM AUTHOR]

Estimation of the degree of semantic similarity/distance between concepts is a very common problem in research areas such as natural language processing, knowledge acquisition, information retrieval or data mining. In the past, many similarity measures have been proposed, exploiting explicit knowledge-such as the structure of a taxonomy-or implicit knowledge-such as information distribution. In the former case, taxonomies and/or ontologies are used to introduce additional semantics; in the latter case, frequencies of term appearances in a corpus are considered. Classical measures based on those premises suffer from some problems: in the first case, their excessive dependency of the taxonomical/ontological structure; in the second case, the lack of semantics of a pure statistical analysis of occurrences and/or the ambiguity of estimating concept statistical distribution from term appearances. Measures based on Information Content (IC) of taxonomical concepts combine both approaches. However, they heavily depend on a properly pre-tagged and disambiguated corpus according to the ontological entities in order to compute accurate concept appearance probabilities. This limits the applicability of those measures to other ontologies -like specific domain ontologies- and massive corpus -like the Web-. In this paper, several of the presented issues are analyzed. Modifications of classical similarity measures are also proposed. They are based on a contextualized and scalable version of IC computation in the Web by exploiting taxonomical knowledge. The goal is to avoid the measures' dependency on the corpus pre-processing to achieve reliable results and minimize language ambiguity. Our proposals are able to outperform classical approaches when using the Web for estimating concept probabilities. [ABSTRACT FROM AUTHOR]

Named Entities Recognition (NER) has become one of the major issues in Information Retrieval (IR), knowledge extraction, and document classification. This paper addresses a particular case of NER, acronym expansion (or definition) when this expansion does not exist in the document using the acronym. Since acronyms may obviously expand into several distinct sets of words, this paper provides nine quality measures of the relevant definition prediction based on mutual information (MI), cubic MI (MI3), and Dice's coefficient. A combinaison of these statistical measures with the cosine approach is proposed. Experiments have been run on biomedical domain where acronyms are numerous. The results on our biomedical corpus showed that the proposed measures were accurate devices to predict relevant definitions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present the design, implementation and evaluation of SOBA, a system for ontology-based information extraction from heterogeneous data resources, including plain text, tables and image captions. SOBA is capable of processing structured information, text and image captions to extract information and integrate it into a coherent knowledge base. To establish coherence, SOBA interlinks the information extracted from different sources and detects duplicate information. The knowledge base produced by SOBA can then be used to query for information contained in the different sources in an integrated and seamless manner. Overall, this allows for advanced retrieval functionality by which questions can be answered precisely. A further distinguishing feature of the SOBA system is that it straightforwardly integrates deep and shallow natural language processing to increase robustness and accuracy. We discuss the implementation and application of the SOBA system within the SmartWeb multimodal dialog system. In addition, we present a thorough evaluation of the different components of the system. However, an end-to-end evaluation of the whole SmartWeb system is out of the scope of this paper and has been presented elsewhere by the SmartWeb consortium. [Copyright &y& Elsevier]

Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (Resource Description Framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied to discover the underlying relation association patterns on RDF metadata. For pruning the large number of redundant overgeneralized patterns in relation pattern search space, the GP-Close algorithm adopts the notion of generalization closure for systematic overgeneralization reduction. The efficacy of our approach is demonstrated through empirical experiments conducted on an online database of terrorist activities. [ABSTRACT FROM AUTHOR]

Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx. • A novel Information Extraction pipeline for mining scientific literature. • Suggestion of cause or treat relations between food and disease entities. • Linking food and disease entities from various knowledge bases. [ABSTRACT FROM AUTHOR]

This paper explores the problem of media content data analysis with the focus on the phenomenon of vaccination, closely related to the COVID-19 pandemic. The presented research is an extension of the previous work, but it differs in two main areas. Firstly, the text corpus submitted to the analysis has been considerably increased. Secondly, the previous data analysis was performed on the body part of the posts, while now it is focused on the most prominent part of the news posts, their headlines. This change from body to headline analysis was provoked by significant differences in their characteristics and the fact that most people read only headlines. Described data acquisition uses an advanced content collection approach followed by the modeling process, during which a set of natural language processing algorithms were applied. To enable the comparison, the model uses the same set of algorithms in the modeling phase like in previous work. The main contributions of the work are manifested in: i) approaching the problem from a new perspective, ii) applying more efficient method of data collection, and crucially iii) enabling the comparison of analysis results for individual parts of the content, which ensured a comprehensive insight into the characteristics of news posts.

The use of information technology in the teaching of English majors in colleges and universities enriches the teaching means, especially the data mining technology that optimizes the digital teaching method. In this paper, starting from English writing, based on the functional requirements of writing training, we constructed a writing training process and utilized the NMF method to mine and decompose the English news text topics. In conjunction with the writing training, the data mining technology centered on the Rete algorithm was used to carry out automatic diagnosis of English composition on the basis of natural language processing. On the basis of mining and analyzing the English news text, the effect of English writing scoring was compared, and its enhancement effect on English composition was verified through controlled tests. The average writing score of the experimental group was 19.62, of which the full score was 25. The mean writing score of the control group is 16.38, which is 3.24 points lower than that of the experimental group. Significance Sig.=0.0004

Introduction: The landscape of drug-drug interactions (DDIs) has evolved significantly over the past 60 years, necessitating a retrospective analysis to identify research trends and under-explored areas. While methodologies like bibliometric analysis provide valuable quantitative perspectives on DDI research, they have not successfully delineated the complex interrelations between drugs. Understanding these intricate relationships is essential for deciphering the evolving architecture and progressive transformation of DDI research structures over time. We utilize network analysis to unearth the multifaceted relationships between drugs, offering a richer, more nuanced comprehension of shifts in research focus within the DDI landscape. Methods: This groundbreaking investigation employs natural language processing, techniques, specifically Named Entity Recognition (NER) via ScispaCy, and the information extraction model, SciFive, to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles spanning January 1962 to July 2023. It reveals key trends and patterns through an innovative network analysis approach. Static network analysis is deployed to discern structural patterns in DDI research, while evolving network analysis is employed to monitor changes in the DDI research trend structures over time. Results: Our compelling results shed light on the scale-free characteristics of pharmacokinetic, pharmacodynamic, and their combined networks, exhibiting power law exponent values of 2.5, 2.82, and 2.46, respectively. In these networks, a select few drugs serve as central hubs, engaging in extensive interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories. Discussion: The finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas. [ABSTRACT FROM AUTHOR]

Simple Summary: Our study provides an overview of the current state of artificial intelligence applications in urooncology and explores potential future advancements in this field. With remarkable progress already achieved, artificial intelligence has revolutionized urooncology by facilitating image analysis, grading, biomarker research, and treatment planning. We also discuss types of artificial intelligence and their possible applications in the management of cancers such as prostate, kidney, bladder, and testicular. As artificial intelligence technology continues to evolve, it holds immense promise for further advancing urooncology and enhancing the care of patients with cancer. Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors. [ABSTRACT FROM AUTHOR]

Featured Application: Hierarchical clause annotation could be applied in many downstream tasks of natural language processing, including abstract meaning representation parsing, semantic dependency parsing, text summarization, argument mining, information extraction, question answering, machine translation, etc. Most natural-language-processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works addressed the issue with the intuition of decomposing complex sentences and linking simple ones, such as rhetorical-structure-theory (RST)-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple sentence decomposition (SSD), etc. However, these works are not applicable for semantic parsing such as abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments with semantic relations and unavailabilities to preserve the original semantics. Following the same intuition and avoiding the deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), for capturing clausal structures of complex sentences, based on the linguistic research of clause hierarchy. With the HCA framework, we annotated a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decomposed HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more-silver annotations. In evaluating the proposed models on our manually annotated HCA dataset, the performances of clause segmentation and parsing resulted in 91.3% F1-scores and 88.5% Parseval scores, respectively. Due to the same model architectures employed, the performance differences of the clause/discourse segmentation and parsing subtasks was reflected in our HCA corpus and compared discourse corpora, where our sentences contained more segment units and fewer interrelations than those in the compared corpora. [ABSTRACT FROM AUTHOR]

Companies continuously perfect product performances directing at consumers' feedback, seeking to enhance customer satisfaction and product competitiveness. To make up for the insufficiency of previous research on product family performance improvement, a method applies multiple data-mining techniques to dig out online reviews is put forward to quantify the improvement priority of each performance in the product family, so as to guide product family improvement. Web Crawler is employed to collect customer reviews of various product variants, and then natural language processing technology is utilized to identify the words expressing functional performances and customer sentiments in the reviews, where the term frequency of each performance is defined as its importance factor. The mapping model between performance specifications and module instances in the product family is established to obtain the commonality factor of each performance. Lexicon-based machine learning is exploited to analyze customers' sentimental values for each performance specification, which is regarded as satisfaction factor. According to the importance and satisfaction of each performance, Kano coefficient is assigned to each performance by utilizing the Kano model. Finally, combined the three factors and Kano coefficient, the improvement priority of each performance specification is estimated to suggest the enterprise to plan the resource allocation for product family improvement. The feasibility of the proposed method is demonstrated by performance improvement for sweeping robot product family and comparison with traditional questionnaire method. [ABSTRACT FROM AUTHOR]

Objective Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: (1) develop a natural language processing information extraction model to capture detailed SDOH information and (2) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. Materials and Methods We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225 089 patients and 430 406 notes with social history sections and compared the extracted SDOH information with existing structured data. Results The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. Conclusions Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs. [ABSTRACT FROM AUTHOR]

Objective The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SDOH and clinical information more broadly. This article presents the shared task, data, participating teams, performance results, and considerations for future work. Materials and Methods The task used the Social History Annotated Corpus (SHAC), which consists of clinical text with detailed event-based annotations for SDOH events, such as alcohol, drug, tobacco, employment, and living situation. Each SDOH event is characterized through attributes related to status, extent, and temporality. The task includes 3 subtasks related to information extraction (Subtask A), generalizability (Subtask B), and learning transfer (Subtask C). In addressing this task, participants utilized a range of techniques, including rules, knowledge bases, n-grams, word embeddings, and pretrained language models (LM). Results A total of 15 teams participated, and the top teams utilized pretrained deep learning LM. The top team across all subtasks used a sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1 Subtask B, and 0.889 F1 for Subtask C. Conclusions Similar to many NLP tasks and domains, pretrained LM yielded the best performance, including generalizability and learning transfer. An error analysis indicates extraction performance varies by SDOH, with lower performance achieved for conditions, like substance use and homelessness, which increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, which reduce health risks (protective factors). [ABSTRACT FROM AUTHOR]

This article proposes a methodology that uses machine learning algorithms to extract actions from structured chemical synthesis procedures, thereby bridging the gap between chemistry and natural language processing. The proposed pipeline combines ML algorithms and scripts to extract relevant data from USPTO and EPO patents, which helps transform experimental procedures into structured actions. This pipeline includes two primary tasks: classifying patent paragraphs to select chemical procedures and converting chemical procedure sentences into a structured, simplified format. We employ artificial neural networks such as long short-term memory, bidirectional LSTMs, transformers, and fine-tuned T5. Our results show that the bidirectional LSTM classifier achieved the highest accuracy of 0.939 in the first task, while the Transformer model attained the highest BLEU score of 0.951 in the second task. The developed pipeline enables the creation of a dataset of chemical reactions and their procedures in a structured format, facilitating the application of AI-based approaches to streamline synthetic pathways, predict reaction outcomes, and optimize experimental conditions. Furthermore, the developed pipeline allows for creating a structured dataset of chemical reactions and procedures, making it easier for researchers to access and utilize the valuable information in synthesis procedures. [ABSTRACT FROM AUTHOR]

Organizations have been challenged by the need to process an increasing amount of data, both structured and unstructured, retrieved from heterogeneous sources. Criminal investigation police are among these organizations, as they have to manually process a vast number of criminal reports, news articles related to crimes, occurrence and evidence reports, and other unstructured documents. Automatic extraction and representation of data and knowledge in such documents is an essential task to reduce the manual analysis burden and to automate the discovering of names and entities relationships that may exist in a case. This paper presents SEMCrime, a framework used to extract and classify named-entities and relations in Portuguese criminal reports and documents, and represent the data retrieved into a graph database. A 5WH1 (Who, What, Why, Where, When, and How) information extraction method was applied, and a graph database representation was used to store and visualize the relations extracted from the documents. Promising results were obtained with a prototype developed to evaluate the framework, namely a name-entity recognition with an F-Measure of 0.73 , and a 5W1H information extraction performance with an F-Measure of 0.65 . [ABSTRACT FROM AUTHOR]

With the increasing popularity of large scale Knowledge Graph (KG)s, many applications such as semantic analysis, search and question answering need to link entity mentions in texts to entities in KGs. Because of the polysemy problem in natural language, entity disambiguation is thus a key problem in current research. Existing disambiguation methods have considered entity prominence, context similarity and entity-entity relatedness to discriminate ambiguous entities, which are mainly working on document or paragraph level texts containing rich contextual information, and based on lexical matching for computing context similarity. When meeting short texts containing limited contextual information, such as web queries, questions and tweets, those conventional disambiguation methods are not good at handling single entity mention and measuring context similarity. In order to enhance the performance of disambiguation methods based on context similarity with such short texts, we propose SCSNED method for disambiguation based on semantic similarity between contextual words and informative words of entities in KGs. Specially, we exploit the effectiveness of both knowledge-based and corpus-based semantic similarity methods for entity disambiguation with SCSNED. Moreover, we propose a Category2Vec embedding model based on joint learning of word and category embedding, in order to compute word-category similarity for entity disambiguation. We show the effectiveness of these proposed methods with illustrative examples, and evaluate their effectiveness in a comparative experiment for entity disambiguation in real world web queries, questions and tweets. The experimental results have identified the effectiveness of different semantic similarity methods, and demonstrated the improvement of semantic similarity methods in SCSNED and Category2Vec over the conventional context similarity baseline. We further compare the proposed approaches with the state of the art entity disambiguation systems and show the performances of the proposed approaches are among the best performing systems. In addition, one important feature of the proposed approaches using semantic similarity, is the potential application on any existing KGs since they mainly use common features of entity descriptions and categories. Another contribution of the paper is an updated survey on background of entity disambiguation in KGs and semantic similarity methods. [ABSTRACT FROM AUTHOR]

We survey the AI research carried out in Greece recently. We concentrate on the case of linked geospatial data, an area with significant practical importance, very interesting research results, and implemented systems developed by a Greek research team. [ABSTRACT FROM AUTHOR]

In NLP data drives research, as evidenced by the frequency with which seminal works of database engineering such as the Penn Treebank have been employed as a basis for experimentation. Traditionally large-scale expertly annotated corpora are expensive and time consuming to produce. This paradigm drove researchers to adopt automated methods for generating labeled data with available tools such as Freebase, DBpedia, and the “infoboxes” found on Wikipedia pages. These knowledge bases have been, or are in the process of being, subsumed by Wikidata, an initiative to concentrate such disparate data repositories in an organized machine readable format. This resource is an important research tool. In this paper, we review our experience using Wikidata in constructing a large annotated corpus under distant supervision, moreover we make the materials, the code used to generate our annotations, freely available to all interested parties. [ABSTRACT FROM AUTHOR]

[Display omitted] • We attach a top-down path to the feature Pyramid Network (FPN). • We designed a fast table parsing method (GTab). • A novel semantic similarity matching model is designed. • A translation-based graph structure-driven Q&A approach is proposed. The geological reports contain various tables, and can offer mineral element content data and stratum detailed information. Geological tabular information extraction and its semantic fusion with text is of great significance in converting and fusing geological unstructured data into structured knowledge to guide cognitive intelligence analysis in the geoscience domain. While the performance of general tools and existing table structure analysis methods is limited due to the various merged cells and diagonally split table headers. To address this issue, we propose a novel approach based on the improved Mask R-CNN model to identify and parse the forms. The geological table parsing network constructed in this paper consists of two key steps: (1) A cell feature augmentation (CFA) module to learn the contextual features for identifying cells of different sizes. (2) A table parsing method (GTab) to parse the table header cells with split lines. We compare the proposed method with commonly used table parsing methods on our constructed geological table dataset. Our models are easily integrated into a prototype system to provide joint information processing and analysis. [ABSTRACT FROM AUTHOR]

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers' preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity. [ABSTRACT FROM AUTHOR]

Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area. [ABSTRACT FROM AUTHOR]

The rapid growth of the cryptocurrency market has led to an increasing interest in the subject. Cryptocurrency is now recognized as an asset, and laws and financial regulations have begun to emerge for supporting its practical use. As a result, it has become essential to perform data mining and attain knowledge from text data related to cryptocurrency. Previous studies have focused on analyzing data from a single source such as Twitter. However, there are unique insights to be gained from data across multiple platforms. In the present study, we utilized data mining techniques to extract insights from LexisNexis, Web of Science, and Reddit, representing the media, academia, and general public, respectively. Among unsupervised learning technologies, topic modeling was employed for the analysis. Topic modeling is a methodology that uncovers hidden meanings within the collected data. Among the diverse topic modeling techniques available, bidirectional encoder representations from transformers topic was chosen for the analysis. BERTopic considered to be state-of-the-art in the field of topic modeling. Dynamic topic modeling was employed to track changes in themes over time. Our experimental results reveal a tendency in the news to cover major events related to cryptocurrencies, such as regulatory developments and market trends. Academic papers, on the other hand, tend to focus on the technology behind cryptocurrencies and related research. Finally, social media conversations center more around information delivery from an investor’s psychological perspective, such as market sentiment and investment strategies.

Data Cleaning is a subfield of Data Mining that is thriving in the recent years. Ensuring the reliability of data, either when generated or received, is of vital importance to provide the best services possible to users. Accomplishing the aforementioned task is easier said than done, since data are complex, generated at an extremely high rate and are of enormous size. A variety of techniques and methods that are part of other subfields from the domain of the Computer Science have been invoked to assist in making Data Cleaning the most efficient and effective possible. Those subfields include, among others, Natural Language Processing (NLP), which in essence refers to the interaction among computers and human language, seeking to find a way to program computers to be able to process and analyze huge volumes of human language data. NLP is a concept that exists for a long time, but, as time goes by, it is proposed that it can be applied to a variety of concepts that are not solely NLP-related. In this paper, a rule-based data cleaning mechanism is proposed, which utilizes NLP to ensure data reliability. Making use of NLP enabled the mechanism not only to be extremely effective but also to be a lot more efficient compared to other corresponding mechanisms that do not utilize NLP. The mechanism was evaluated upon diverse healthcare datasets, not however being limited to the healthcare domain, but supporting a generalized data cleaning concept.

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F 1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation. [ABSTRACT FROM AUTHOR]

As the prevalence of social media on the Internet, opinion mining has become an essential approach to analyzing so many data. Various applications appear in a wide range of industrial domains. Meanwhile, opinions have diverse expressions which bring along research challenges. Both of the practical demands and research challenges make opinion mining an active research area in recent years. In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce general NLP techniques which are required for text preprocessing. Second, we investigate the approaches of opinion mining for different levels and situations. Then we introduce comparative opinion mining and deep learning approaches for opinion mining. Opinion summarization and advanced topics are introduced later. Finally, we discuss some challenges and open problems related to opinion mining. [ABSTRACT FROM AUTHOR]

Medical Health Records serves as rich knowledge sources for data mining. The availability of enormous amounts of medical data can be utilized to extract constructive facts using various data mining techniques. Numerous researches have been conducted in the field of medical data mining. In this paper, a novel approach is suggested to extract association rules from medical records by choosing the best association rule mining algorithm using multiple-criteria decision analysis. The purpose of the project is to discover correlation between diseases, diseases and symptoms, diseases and medicines. [ABSTRACT FROM AUTHOR]

Inference approaches in Arabic question answering systems are in their first steps if we compare them with other languages. Evidently, any user is interested in obtaining a specific and precise answer to a specific question. Therefore, the challenge of developing a system capable of obtaining a relevant and concise answer is obviously of great benefit. This paper deals with answering questions about temporal information involving several forms of inference. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach to understand situations in the real world with the sentiment analysis of Twitter data base on deep learning techniques. With the proposed method, it is possible to predict user satisfaction of a product, happiness with some particular environment or destroy situation after disasters. Recently, deep learning is able to solve problems in computer vision or voice recognition, and convolutional neural network (CNN) works good for image analysis and image classification. The biggest reason to adopt CNN in image analysis and classification is due to CNN can extract an area of features from global information, and it is able to consider the relationship among these features. The above solution can achieve a higher accuracy in analysis and classification. For natural language processing, texts data features also can be extracted piece by piece and to consider the relationship among these features, but without the consideration of context or whole sentence, the sentiment might be understood wrong. And currently, convolutional neural network is one of the most effective methods to do image classification, CNN has a convolutional layer to extract information by a larger piece of text, so we work for sentiment analysis with convolutional neural network, and we design a simple convolutional neural network model and test it on benchmark, the result shows that it achieves better accuracy performance in twitter sentiment classification than some of traditional method such as the SVM and Naive Bayes methods. [ABSTRACT FROM AUTHOR]

A Named Entity Recognition (NER) plays a noteworthy role in Natural Language Processing (NLP) research, since it makes available the detection of proper nouns in unstructured texts. NER makes easier searching, retrieving, and extracting information seeing as the significant information in texts is usually sited around proper names. This paper suggests an efficient approach that can identify Named Entities (NE) in Arabic texts without the need for morphological or syntactic analysis or gazetteers. The goal of our approach is to provide a general framework for Arabic NE recognition. Within this framework; the system learns the recognition of NE automatically and induces NE systematically, starting from sample NE instances as seeds. This method takes advantage from the web, the approach learns from a web corpus. The seeds are used to identify the contexts in the web denoting NE and then the contexts identify new NE. Thorough experimental evaluation of our approach, the performances measured by recall, precision and f-measure conducted to recognize NE are promising. We obtained an overall rate of F-measure equal to 83%. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is a fundamental task useful for Information Retrieval, Information Extraction, web search, and indexing, among others. In the literature there exist several works dedicated to generic WSD task, but in recent years domain-specific WSD has attracted the attention of several researchers. In this sense, this paper describes an approach for domain-specific WSD by selecting the predominant sense (synset from WordNet) of ambiguous words. To achieve it the method uses two corpora: the domain-specific test corpus (containing target ambiguous words) and a domain-specific auxiliary corpus (obtained by using relevant words from the domain-specific test corpus ). The approach has four main stages: (1) auxiliary corpus generation; (2) related features extraction (from the auxiliary corpus); (3) test features extraction (from the test corpus); and (4) features integration . The proposed approach has been tested on domain-specific corpora (Sports and Finance) and on one balanced corpus, BNC. Even though our WSD approach showed some limitations when dealing with the general-domain corpus, the obtained results for domain-specific corpora, which are our main interest, were better than those reported in previous works. [ABSTRACT FROM AUTHOR]

The rate at which information about music is being created and shared on the web is growing exponentially. However, the challenge of making sense of all this data remains an open problem. In this paper, we present and evaluate an Information Extraction pipeline aimed at the construction of a Music Knowledge Base. Our approach starts off by collecting thousands of stories about songs from the songfacts.com website. Then, we combine a state-of-the-art Entity Linking tool and a linguistically motivated rule-based algorithm to extract semantic relations between entity pairs. Next, relations with similar semantics are grouped into clusters by exploiting syntactic dependencies. These relations are ranked thanks to a novel confidence measure based on statistical and linguistic evidence. Evaluation is carried out intrinsically, by assessing each component of the pipeline, as well as in an extrinsic task, in which we evaluate the contribution of natural language explanations in music recommendation. We demonstrate that our method is able to discover novel facts with high precision, which are missing in current generic as well as music-specific knowledge repositories. [ABSTRACT FROM AUTHOR]

The financial business process worldwide suffers from huge dependencies upon labor and written documents, thus making it tedious and time-consuming. In order to solve this problem, traditional robotic process automation (RPA) has recently been developed into a hyper-automation solution by combining computer vision (CV) and natural language processing (NLP) methods. These solutions are capable of image analysis, such as key information extraction and document classification. However, they could improve on text-rich document images and require much training data for processing multilingual documents. This study proposes a multimodal approach-based intelligent document processing framework that combines a pre-trained deep learning model with traditional RPA used in banks to automate business processes from real-world financial document images. The proposed framework can perform classification and key information extraction on a small amount of training data and analyze multilingual documents. In order to evaluate the effectiveness of the proposed framework, extensive experiments were conducted using Korean financial document images. The experimental results show the superiority of the multimodal approach for understanding financial documents and demonstrate that adequate labeling can improve performance by up to about 15%. [ABSTRACT FROM AUTHOR]

Many unstructured documents contain segments with specific topics. Extracting these segments and identifying their topics helps to access the required information directly. This can improve the quality of many NLP applications such as information extraction, information retrieval, summarization, and question answering. Resumes (CVs) are unstructured documents that have diverse formats. They contain various segments such as personal information, experience, and education. Manually processing resumes to find the most suitable candidates for a particular job is a difficult task. Due to the increased amount of data, it has become very necessary to manipulate resumes by computer to save time and effort. This research presents a new algorithm named TSHD for topic segmentation based on headings detection. We apply the algorithm to extract resume segments and identify their topics. The proposed TSHD algorithm is accurate and addresses many weaknesses in previous studies. Evaluation results show a very high F1 score (about 96%) and a very low segmentation error (about 2%). The algorithm can be easily adapted to deal with other textual domains that contain headings in their segments. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning and natural language processing (NLP) have opened many new opportunities for automatic text understanding and text processing in the medical field. This is of great benefit as many clinical downstream tasks rely on information from unstructured clinical documents. However, for low-resource languages like German, the use of modern text processing applications that require a large amount of training data proves to be difficult, as only few data sets are available mainly due to legal restrictions. In this study, we present an information extraction framework that was initially pre-trained on real-world computed tomographic (CT) reports of head examinations, followed by domain adaptive fine-tuning on reports from different imaging examinations. We show that in the pre-training phase, the semantic and contextual meaning of one clinical reporting domain can be captured and effectively transferred to foreign clinical imaging examinations. Moreover, we introduce an active learning approach with an intrinsic strategic sampling method to generate highly informative training data with low human annotation cost. We see that the model performance can be significantly improved by an appropriate selection of the data to be annotated, without the need to train the model on a specific downstream task. With a general annotation scheme that can be used not only in the radiology field but also in a broader clinical setting, we contribute to a more consistent labeling and annotation process that also facilitates the verification and evaluation of language models in the German clinical setting. [ABSTRACT FROM AUTHOR]

In recent years, joint triple extraction methods have received extensive attention because they have significantly promoted the progress of information extraction and many related downstream tasks in the field of natural language processing. However, due to the inherent complexity of language such as relation overlap, joint extraction model still faces great challenges. Most of the existing models to solve the overlapping problem adopt the strategy of constructing complex semantic shared encoding features with all types of relations, which makes the model suffer from redundancy and poor inference interpretability in the prediction process. Therefore, we propose a new model for entity role attribute recognition based on triple holistic fusion features, which can extract triples (including overlapping triples) under a limited number of relationships, and its prediction process is simple and easy explain. We adopt the strategy of low-level feature separation and high-level concept fusion. First, we use the low-level token features to perform entity and relationship prediction in parallel, then use the residual connection with attention calculation to perform feature fusion on the candidate triples in the entity-relation matrix, and finally determine the existence of triple by identifying the entity role attributes. Experimental results show that the proposed model is very effective and achieves state-of-the-art performance on the public datasets. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is a basic but crucial task in the field of natural language processing (NLP) and big data analysis. The recognition of named entities based on Chinese is more complicated and difficult than English, which makes the task of NER in Chinese more challenging. In particular, fine-grained named entity recognition is more challenging than traditional named entity recognition tasks, mainly because fine-grained tasks have higher requirements for the ability of automatic feature extraction and information representation of deep neural models. In this paper, we propose an innovative neural network model named En2BiLSTM-CRF to improve the effect of fine-grained Chinese entity recognition tasks. This proposed model including the initial encoding layer, the enhanced encoding layer, and the decoding layer combines the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. Hence, it can encode information multiple times and extract contextual features hierarchically. We conducted sufficient experiments on two representative datasets using multiple important metrics and compared them with other advanced baselines. We present promising results showing that our proposed En2BiLSTM-CRF has better performance as well as better generalization ability in both fine-grained and coarse-grained Chinese entity recognition tasks. [ABSTRACT FROM AUTHOR]

• Propose a hybrid Transformer approach for Chinese NER. • Take the latent words as the additional features to improve NER task. • Find an efficient strategy to fine-tune BERT with other modules. • Apply relative position encodings to improve Transformer. Named entity recognition (NER) plays an important role in many downstream tasks of natural language processing, such as knowledge extraction and information retrieval. NER of Chinese is more challenging than that of English due to lack of the explicit word boundary. Features augmentation is a potential way to improve NER model of Chinese. Pre-trained models can implicitly preserve prior knowledge with additional features. This paper proposes a hybrid Transformer approach, which first utilize the fused additional features embeddings (e.g. char embeddings, bigram embeddings, lattice embeddings and BERT embeddings) as distributed representations to augment the representation ability of model. In addition, a new training strategy named DF strategy is proposed to efficiently fine-tune Bidirectional Encoder Representations from Transformers (BERT) and other embeddings in balance. Then, the proposed model can perceive the relations of features by introducing relative position embeddings to an additional adapted Transformer encoder. Lastly, a standard Conditional Random Field is used to alleviate the obvious tag errors. The proposed model is applied to four representative Chinese datasets to investigate its performance. Experiments results show that the proposed model outperforms the other popular models in terms of accuracy. The proposed BL-BTC model can effectively improve the recognition performance of formal and informal texts. [ABSTRACT FROM AUTHOR]