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Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human's natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document's main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms' order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm. • Semantic similarity affects the overall performance of knowledge-based Word Sense Disambiguation (WSD) systems. • With Semantic similarity, sense heuristics, and document context, we designed a novel knowledge-based word sense disambiguation algorithm. • The Sequential Contextual Similarity Matrix Multiplication (SCSMM) algorithm captures the maximum sentence context while maintaining the words' order. • The SCSMM algorithm outperforms current WSD systems when disambiguating nouns. [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]

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]

In this paper, we learn a Contrastive Feature Distribution Model (CFDM) for interaction recognition. Our contributions are three-folded. First of all, we introduce an intra–inter-frame skeleton feature for interaction description. Secondly, we learn CFDM for a discriminative representation of interactions. In this step, we mine contrastive features to create a dictionary, and learn the probability distribution of dictionary words to construct CFDM in positive and negative training samples. With CFDM, we represent interactions in a discriminative way for recognition. Since there is few skeleton based interaction databases now, we capture a new database, CR-UESTC, which is the third contribution. We evaluate the proposed CFDM approach on CR-UESTC and SBU interaction databases, and compare the result of CFDM with the CM and the BoW approach. The comparison indicates that the recognition accuracy of three approaches is: CFDM > CM > BoW. Compared with Yun et al. (2012), the proposed CFDM also obtain a better result on SBU database. [ABSTRACT FROM AUTHOR]

• Propose a large-scale query-based document summarization dataset. • Three models for solving the problem. • Data and experiment analysis; • Online large-scale A/B testing in real-world mobile applications. Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM , a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field. [ABSTRACT FROM AUTHOR]