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With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

This paper examines in close relation two fields of growing importance: model‐based systems engineering (MBSE) and natural language processing (NLP). System models provide a structured description of engineering data, whose inherent semantics often remains hard to explore. Natural language understanding, (i.e., the machine analysis of texts produced by humans) an important field of NLP, focuses on semantic text comprehension but cannot directly account for structured information sources. In this paper, we investigate natural language understanding of MBSE artifacts as they appear in industrial scenarios. In this context, the wide and heterogeneous knowledge space and user base calls for novel techniques to facilitate information retrieval from complex system models. We thus propose to leverage domain‐specific text generators to transform models into a descriptive text corpus on which we apply state‐of‐the‐art semantic search and analysis techniques. We illustrate the approach on relevant MBSE examples by performing a qualitative evaluation of intuitive text search and comprehension in textual descriptions obtained from system models. [ABSTRACT FROM AUTHOR]

Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the Web. This paper focuses on addressing the lexical gap problem in question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings a new challenge for question retrieval in cQA. In this paper, we propose to model and learn distributed word representations with metadata of category information within cQA pages for question retrieval using two novel category powered models. One is a basic category powered model called MB-NET and the other one is an enhanced category powered model called ME-NET which can better learn the distributed word representations and alleviate the lexical gap problem. To deal with the variable size of word representation vectors, we employ the framework of fisher kernel to transform them into the fixed-length vectors. Experimental results on large-scale English and Chinese cQA data sets show that our proposed approaches can significantly outperform state-of-the-art retrieval models for question retrieval in cQA. Moreover, we further conduct our approaches on large-scale automatic evaluation experiments. The evaluation results show that promising and significant performance improvements can be achieved. [ABSTRACT FROM PUBLISHER]

Purpose With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources.Design/methodology/approach First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree.Findings The evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query.Originality/value Existing NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases. [ABSTRACT FROM AUTHOR]

Text visualization has become a significant tool that facilitates knowledge discovery and insightful presentation of large amounts of data. This paper presents a visualization system for exploring Arabic text called ViStA. We report about the design, the implementation and some of the experiments we conducted on the system. The development of such tools assists Arabic language analysts to effectively explore, understand, and discover interesting knowledge hidden in text data. We used statistical techniques from the field of Information Retrieval to identify the relevant documents coupled with sophisticated natural language processing (NLP) tools to process the text. For text visualization, the system used a hybrid approach combining latent semantic indexing for feature selection and multidimensional scaling for dimensionality reduction. Initial results confirm the viability of using this approach to tackle the problem of Arabic text visualization and other Arabic NLP applications. [ABSTRACT FROM AUTHOR]

More than ever, technical inventions are the symbol of our society’s advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality. [ABSTRACT FROM AUTHOR]

Twitter has attracted millions of users to share and disseminate most up-to-date information, resulting in large volumes of data produced everyday. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. By splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo feedback. Experiments on two tweet data sets show that tweet segmentation quality is significantly improved by learning both global and local contexts compared with using global context alone. Through analysis and comparison, we show that local linguistic features are more reliable for learning local context compared with term-dependency. As an application, we show that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging. [ABSTRACT FROM PUBLISHER]

As a valuable tool for text understanding, semantic similarity measurement enables discriminative semantic-based applications in the fields of natural language processing, information retrieval, computational linguistics and artificial intelligence. Most of the existing studies have used structured taxonomies such as WordNet to explore the lexical semantic relationship, however, the improvement of computation accuracy is still a challenge for them. To address this problem, in this paper, we propose a hybrid WordNet-based approach CSSM-ICSP to measuring concept semantic similarity, which leverage the information content(IC) of concepts to weight the shortest path distance between concepts. To improve the performance of IC computation, we also develop a novel model of the intrinsic IC of concepts, where a variety of semantic properties involved in the structure of WordNet are taken into consideration. In addition, we summarize and classify the technical characteristics of previous WordNet-based approaches, as well as evaluate our approach against these approaches on various benchmarks. The experimental results of the proposed approaches are more correlated with human judgment of similarity in term of the correlation coefficient, which indicates that our IC model and similarity detection approach are comparable or even better for semantic similarity measurement as compared to others. [ABSTRACT FROM AUTHOR]

Given its generality in applications and its high time-efficiency on big data-sets, in recent years, the technique of text filtering through pattern matching has been attracting increasing attention from the field of information retrieval and Natural language Processing (NLP) research communities at large. That being the case, however, it has yet to be seen how this technique and its algorithms, (e.g., Wu–Manber, which is also considered in this paper) can be applied and adopted properly and effectively to Uyghur, a low-resource language that is mostly spoken by the ethnic Uyghur group with a population of more than eleven-million in Xinjiang, China. We observe that technically, the challenge is mainly caused by two factors: (1) Vowel weakening and (2) mismatching in semantics between affixes and stems. Accordingly, in this paper, we propose Wu–Manber–Uy, a variant of an improvement to Wu–Manber, dedicated particularly for working on the Uyghur language. Wu–Manber–Uy implements a stem deformation-based pattern expansion strategy, specifically for reducing the mismatching of patterns caused by vowel weakening and spelling errors. A two-way strategy that applies invigilation and control on the change of lexical meaning of stems during word-building is also used in Wu–Manber–Uy. Extra consideration with respect to Word2vec and the dictionary are incorporated into the system for processing Uyghur. The experimental results we have obtained consistently demonstrate the high performance of Wu–Manber–Uy. [ABSTRACT FROM AUTHOR]

Clinical narratives commonly use acronyms without explicitly defining their long forms. This makes it difficult to automatically interpret their sense as acronyms tend to be highly ambiguous. Supervised learning approaches to their disambiguation in the clinical domain are hindered by issues associated with patient privacy and manual annotation, which limit the size and diversity of training data. In this study, we demonstrate how scientific abstracts can be utilised to overcome these issues by creating a large automatically annotated dataset of artificially simulated global acronyms. A neural network trained on such a dataset achieved the F1-score of 95% on disambiguation of acronym mentions in scientific abstracts. This network was integrated with multiword term recognition to extract a sense inventory of acronyms from a corpus of clinical narratives on the fly. Acronym sense extraction achieved the F1-score of 74% on a corpus of radiology reports. In clinical practice, the suggested approach can be used to facilitate development of institution-specific inventories. [ABSTRACT FROM AUTHOR]

Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook longdistance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSAGCN, leveraging a graph convolutional network to effectively capture both shortand long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation. [ABSTRACT FROM AUTHOR]

This paper explores the relationships between natural language lexicons in lexical semantics and thesauri in information retrieval research. These different areas of knowledge have different restrictions on use of vocabulary; thesauri are used only in information search and retrieval contexts, whereas lexicons are mental systems and generally applicable in all domains of life. A set of vocabulary requirements that defines the more concrete characteristics of vocabulary items in the 2 contexts can be derived from this framework: lexicon items have to be learnable, complex, transparent, etc., whereas thesaurus terms must be effective, current and relevant, searchable, etc. The differences in vocabulary properties correlate with 2 other factors, the well-known dimension of Control (deliberate, social activities of building and maintaining vocabularies), and Syntagmatization, which is less known and describes vocabulary items' varying formal preparedness to exit the thesaurus/lexicon, enter into linear syntactic constructions, and, finally, acquire communicative functionality. It is proposed that there is an inverse relationship between Control and Syntagmatization. [ABSTRACT FROM AUTHOR]

Background and Objective: Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary.Methods: In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine-learning algorithms. Finally, the class label is predicted using the trained classifiers.Results: Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features' provider of support vector machine (SVM) lead to the highest accuracy of 89.40 %.Conclusion: The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems. [ABSTRACT FROM AUTHOR]

This work focuses on determining semantically close words and using semantic similarity in general in order to improve performance in information retrieval tasks. The semantic similarity of words is an important task with many applications from information retrieval to spell checking or even document clustering and classification. Although, in languages with rich linguistic resources, the methods and tools for this task are well established, some languages do not have such tools. The first step in our experiment is to represent the words in a collection in a vector form and then define the semantic similarity of the terms using a vector similarity method. In order to tame the complexity of the task, which relies on the number of word (and, consequently, of the vector) pairs that have to be combined in order to define the semantically closest word pairs, A distributed method that runs on Apache Spark is designed to reduce the calculation time by running comparison tasks in parallel. Three alternative implementations are proposed and tested using a list of target words and seeking the most semantically similar words from a lexicon for each one of them. In a second step, we employ pre-trained multilingual sentence transformers to capture the content semantics at a sentence level and a vector-based semantic index to accelerate the searches. The code is written in MapReduce, and the experiments and results show that the proposed methods can provide an interesting solution for finding similar words or texts in the Kazakh language. [ABSTRACT FROM AUTHOR]

This study aims to explore research collaborations and authorship patterns in the field of semantic digital libraries(SDL). The data is extracted (N=2075) from the Scopus database using keywords related to semantic digital libraries by considering all types of publications during 1983-2019. The analysis of each document is based on the following scientometrics indicators: author productivity, degree of collaboration, collaboration index, collaboration coefficient and modified collaboration coefficient. Correlation matrices were also calculated and inferences drawn in terms of authors and publications. A network visualisation tool VOSviewer was used to present authorship correlation network strength and keyword mapping for a better insight into the emerging areas in the field of SDL. The resulting average degree of collaboration of 0.898 indicates that a large number of publications are multi-authored and that there is a higher level of collaborative research in the field of semantic digital libraries. Meghini C from the Institute of Information Science and Technologies, Italy has produced the highest number of research paper(n=18) whereas Egenhofer MJ found to be a profoundly impacted author with 851 citations on in the studied domain. Results also reveal that the focus areas of research related to SDL include digital libraries, semantic web, ontology, metadata and information retrieval. However, keywords such as natural language processing system, computational linguistics, linked data are also repeated frequently in the published literature, thus revealing the emerging areas of the future research in the domain of SDL. [ABSTRACT FROM AUTHOR]

Semantic similarity measures play an important role in many natural language processing and information retrieval activities. It is highly challenging to measure semantic similarity with higher accuracy. A notable branch of semantic similarity evaluation based on information content (IC) is popular in this aspect. Intrinsic information content (IIC) models are another wing of IC based evaluation. Both IC based and IIC based approaches majorly handled similarity evaluation of nouns. Research related to semantic similarity assessment of verb pairs are rarely discussed. To bridge this gap, this work examines various IC based, IIC based approaches on verb pairs. A detailed discussion of the existing measures and their drawbacks are mentioned in this work. Strategies based on information content, length and depth of the concepts are discussed and tested on benchmark datasets. Existing intrinsic information content models are enhanced by addressing various issues like (a) dealing concepts with no path in WordNet and (b) handling the synonym sets of verb concepts. Measures based on path length, intrinsic information content, combined strategies and non-linear strategies for verb pairs are thoroughly inspected. This paper also presents novel strategies to understand novel aspects that are not addressed before. The strategies are experimented by generating the synonym sets of required parts-of-speech which proved very effective in improving the correlation with human judgment. Results on benchmark datasets specify that the proposed approaches for verb similarity will be a guiding factor for understanding the natural language processing tasks. • Semantic similarity measures to address the verb pairs. • Path-based, information content based, combined and non-linear strategies for evaluating semantic similarity. • Intrinsic information content models for evaluating semantic similarity. • In this paper, handling the word pairs which does not have path in the semantic network proved effective. • Generating proper synonym sets of the concepts, improved the correlation of the strategies proposed. [ABSTRACT FROM AUTHOR]

The ubiquitous presence of surveillance systems generates massive amounts of video data. Storage and analysis of this data in real-time is a substantial challenge. There is huge potential in representing data in machine-readable and machine-interpretable format due to the presence of hidden semantics in images and videos. However, such representation requires ontology, which calls for expert domain knowledge. In this paper, a novel NLP-guided approach to generate an ontology for multimedia representation and information retrieval is proposed. A semi-automatic NLP-guided framework, which extracts all possible relations among objects is presented. This framework leverages the textual data of the domain to generate possible descriptions and actions within the domain. Relations among objects get embedded as object properties, whereas the category of an object as a class. Features and attributes of objects encode the data properties of the ontology. The proposed ontology is compared with existing multimedia ontologies and evaluated with regard to its capability to represent relations occurring in benchmark datasets, demonstrating the completeness and thorough coverage of the domain concepts. Spatial reasoning rules are established using Semantic Web Rule Language (SWRL) rules, and information retrieval is demonstrated using Description Logic (DL) and SPARQL queries. The proposed NLP-guided ontology generation approach is general enough to help in the development of ontologies for other domains as well, by providing video and textual data of the domain of interest, with limited human involvement. • Object properties extraction with textbase and NLP-guided approach. • Frequency-based semantic refinement to enhance domain-specific relations. • A novel data-driven video surveillance ontology "VizITS" for the ITS domain. • Semantic information retrieval as events, metadata, and question-answering. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation represents a crucial task in many natural language processing applications such as information extraction, information retrieval and text summarization. It intends to identify the correct sense of an ambiguous term (target word) according to its context. In this paper, we present a biomedical knowledge-based disambiguation method for determining the adequate domain or sub-domain (sense) of an ambiguous biomedical term. This method uses the MeSH thesaurus as a knowledge resource and an intrinsic information content-based semantic similarity to measure the likeness between their different senses of the ambiguous term MeSH and its context. We define this latter as the set of unambiguous MeSH terms characterizing a document. We aim on one hand to minimize the time and computational complexity and on the other hand to increase the accuracy of the disambiguation by using such context. To evaluate this method, we involved it into a document indexing and retrieval system. The results of experiments, carried out on a sub-set of the OHSUMED collection, in terms of Mean Average Precision show that our method performs well. [ABSTRACT FROM AUTHOR]

Hashing based approximate nearest neighbors (ANN) search has drawn considerable attraction owing to its low-memory storage and hardware-level logical computing which is doomed to be greatly applicable to quantities of large-scale and practical scenarios, such as information retrieval, computer vision and natural language processing. However, most existing hashing methods concentrate either on images only or on pairwise image-texts (labels, short documents) and rarely utilize more common sentences. In this paper, we propose D iversity R egularized L atent S emantic M atch for H ashing (DRLSMH), a new multimodal hashing method that projects images and sentences into a shared latent semantic space with label-supervised semantic constraints to proceed on multimodal retrieval. Notably, soft orthogonality is induced as a novel regularizer to preserve diverse hashing functions for compact and accurate representations; what's more, this kind of regularization also benefits the derivations of closed-form solutions with some proper relaxations under iterative optimization framework. Extensive experiments on two public datasets demonstrate the advantages of our method over some state-of-the-art baselines under cross-modal retrieval both on image-query-image, image-query-text and text-query-image tasks. [ABSTRACT FROM AUTHOR]

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model—GatorTron—using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og. [ABSTRACT FROM AUTHOR]

The Semantic Web has emerged as an extension of the current Web, in which Web content has well-defined meaning through the addition of logic-based metadata. However, current mechanisms for information retrieval from semantic knowledge bases restrict their use to only experienced users. To address this gap, the natural language processing (NLP) is deemed to be very intuitive from a use point of view, due to it hides the formality of a knowledge base as well as the executable query language. This paper presents a novel ontology-based information retrieval system for DBpedia called ONLI (Ontology-based Natural Language Interface). ONLI proposes the use of an ontology model in order to represent both the syntactic question’s structure and the question’s context. This model allows inferring the answer type expected by the user through an established question’s classification. These features allow reducing the search space thus increasing the probability of providing the correct answer. From this perspective, ONLI was evaluated in terms of their ability to find the correct answer into DBpedia’s content, achieving promising results and proving to be very useful to non-experienced users. [ABSTRACT FROM AUTHOR]

High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance. [ABSTRACT FROM AUTHOR]

Word sense disambiguation deals with deciding the word's precise meaning in a certain specific context. One of the major problems in natural language processing is lexicalsemantic ambiguity, where a word has more than one meaning. Disambiguating the sense of polysemous words is the most important task in machine translation. This research work aims to design and implement English to Hindi machine translation. The design methodology addresses improving the speed and accuracy of the machine translation process. The algorithm and modules designed in this research work have been deployed on the Hadoop infrastructure, and test cases are designed to check the feasibility and reliability of this process. The research work presented describes the methodologies to reduce data transmission by adding a translation memory component to the framework. The speed of execution is increased by replacing the modules in the machine translation process with lightweight modules, which reduces infrastructure and execution time. [ABSTRACT FROM AUTHOR]

Top-k words selection is a technique used to detect and return the k most similar words to a given word from a candidate set. This is a crucial and widely used tool in various tasks. The key issue in top-k words selection is how to measure the similarity between words. One popular and effective solution is to use a word embedding-based similarity measure, which represents words as low-dimensional vectors and measures the similarities between words according to the similarity of the vectors, using a metric. However, most word embedding methods only consider the local proximity properties of two words in a corpus. To mitigate this issue. In this article, we propose to use association rules for measuring word similarity at a global level, and a fuzzy similarity measure for top-k words selection that jointly encodes the local and the global similarities. Experiments on a real-world query task with three benchmark datasets, i.e., TREC-disk 4&5, WT10G, and RCV1, demonstrate the efficiency of the proposed method compared to several state-of-the-art baselines. [ABSTRACT FROM AUTHOR]