A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches

Navigli, Roberto

doi:10.1007/978-3-642-27660-6_10

Cited by 98 publications

(45 citation statements)

References 49 publications

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“…Obtaining accurate semantic representations of individual word senses or concepts is vital for several applications in Natural Language Processing (NLP) such as, for example, Word Sense Disambiguation (Navigli, 2009;Navigli, 2012), Entity Linking (Bunescu and Paşca, 2006;Rao et al, 2013), semantic similarity (Budanitsky and Hirst, 2006), Information Extraction (Banko et al, 2007), and resource linking and integration (Pilehvar and Navigli, 2014). One prominent semantic representation approach is the distributional semantic model, which represents lexical items as vectors in a semantic space.…”

Section: Introductionmentioning

confidence: 99%

NASARI: a Novel Approach to a Semantically-Aware Representation of Items

Camacho-Collados

Pilehvar

Navigli

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.

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Section: Introductionmentioning

confidence: 99%

NASARI: a Novel Approach to a Semantically-Aware Representation of Items

Camacho-Collados

Pilehvar

Navigli

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…WordNet is indeed rich in information and it is a standout amongst the most commonplace apparatuses for word sense disambiguation [22].…”

Section: Wordnetmentioning

confidence: 99%

Evaluation on knowledge extraction and machine learning in resolving Malay word ambiguity

Yahaya

Rahman

Bakar

et al. 2018

J. Fundam and Appl Sci.

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The involvement of linguistic professionals in resolving the ambiguity of a word within a particular context will produce a concise meaning of the words that are found in the lexical knowledge based collection. Motivated from that issue, we employed lexical knowledge and machine learning approach which includes the integration of data or/and information from the lexical knowledge based, that is Malay collections which linked to the ambiguous words. We show that the proposed method has improved the precision in resolving ambiguity.

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“…Approaches used in WSD can be categorized as supervised, unsupervised, semi-supervised, knowledgebased, bootstrapped, hybrid and dictionary-based approaches [2,16,19,[21][22][23][24]. The Dictionary based approach is the oldest approach and it was proposed by Karov and Edelman [28].…”

Section: Introductionmentioning

confidence: 99%

Ambiguity in Question Paper Translation

Vikram¹,

Dwivedi²

2018

IJMECS

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Abstract-Word sense ambiguity is a prevalent nature of machine translation for various language pairs including English-Hindi language. For example, the word "paper" has several senses which may refer to a question paper, research paper, newspaper, simple paper or a white paper. The specific sense intended is determined by the context in which an instance of the ambiguous word appears. This specific sense which is determined by the context is known as Word Sense Disambiguation (WSD). Translation of question paper is a specific application of MT wherein any type of ambiguity in question may affect the overall meaning of questions. This paper discusses types of ambiguity in the context of question paper translation (English to Hindi) and their impact on translation by analyzing a set of questions taken from National Council of Educational Research and Training (NCERT) and some other resources.

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A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches

Cited by 98 publications

References 49 publications

NASARI: a Novel Approach to a Semantically-Aware Representation of Items

NASARI: a Novel Approach to a Semantically-Aware Representation of Items

Evaluation on knowledge extraction and machine learning in resolving Malay word ambiguity

Ambiguity in Question Paper Translation

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