Word sense disambiguation

Navigli, Roberto

doi:10.1145/1459352.1459355

Cited by 1,405 publications

(1,030 citation statements)

References 130 publications

Supporting

Mentioning

1,004

Contrasting

Unclassified

Order By: Relevance

“…Finally, it may also be helpful to mention that, in natural language processing, there is a substantial amount of work on the word sense disambiguation (WSD) problem (see, for example, [28] for a recent survey) and to emphasise that this is quite different (e.g., in aims) from the work reported here.…”

Section: Resultsmentioning

confidence: 88%

Contextual Analysis of Word Meanings in Type-Theoretical Semantics

Zhang

2011

Logical Aspects of Computational Linguistics

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 88%

Contextual Analysis of Word Meanings in Type-Theoretical Semantics

Zhang

2011

Logical Aspects of Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…With that in mind, we extended this baseline approach calculating a score for each possible CUI (meaning) of an ambiguous term as shown in equation (1).…”

Section: Methodsmentioning

confidence: 99%

“…Word sense disambiguation (WSD) is responsible for solving these ambiguities in textual documents, having the automatic capability to assign the correct meaning to ambiguous words given the surrounding textual context. WSD is a challenging artificial intelligence problem that has been studied for the last years [1]. Particularly in the biomedical field, there are much more ambiguous terms increasing the WSD difficulty [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation

Antunes

Matos

2017

Journal of Integrative Bioinformatics

View full text Add to dashboard Cite

Abstract:Word sense disambiguation (WSD) is an important step in biomedical text mining, which is responsible for assigning an unequivocal concept to an ambiguous term, improving the accuracy of biomedical information extraction systems. In this work we followed supervised and knowledge-based disambiguation approaches, with the best results obtained by supervised means. In the supervised method we used bag-of-words as local features, and word embeddings as global features. In the knowledge-based method we combined word embeddings, concept textual definitions extracted from the UMLS database, and concept association values calculated from the MeSH co-occurrence counts from MEDLINE articles. Also, in the knowledge-based method, we tested different word embedding averaging functions to calculate the surrounding context vectors, with the goal to give more importance to closest words of the ambiguous term. The MSH WSD dataset, the most common dataset used for evaluating biomedical concept disambiguation, was used to evaluate our methods. We obtained a top accuracy of 95.6 % by supervised means, while the best knowledge-based accuracy was 87.4 %. Our results show that word embedding models improved the disambiguation accuracy, proving to be a powerful resource in the WSD task.

show abstract

“…WordNet is a computational lexicon of English, created and maintained at Princeton University. It encodes the concepts in terms of sets of synonyms (called synsets) [8]. Each word sense identifies a single synset and hence is encoded as a single concept.…”

Section: Stage 6: Topic Extractionmentioning

confidence: 99%

Road Map Approach to Automatic Topic Detection of Diaries

Ismail¹

2013

IJMLC

View full text Add to dashboard Cite

Abstract-The famous question on social networks "what is on your mind?" urges many of us to convert their thoughts and feelings into diaries. Detecting the topic of diaries is an interesting task to know people' interest. Many automatic methods have been introduced. The method used in this paper depends on preprocessing the diary words to generate a feature vector for each word. Then, the senses of each word are detected from the diary context by using CBC (clustering by committee) algorithm. CBC can avoid discovering duplicate senses and discover the less frequent senses of a word. The sense with the highest score is selected for each word. To detect the topic of the diary, the whole discovered senses are translated into concepts using the hierarchical concept taxonomy Wordnet. Concepts are traversed from bottom up to reach the most generalized concepts that express the topic of the diary. The previous approaches studied in this paper depend on the idea that the more frequent a word is used, the more important it is. The approach presented differs in that it incorporates the semantic dependencies among words because straightforward word counting misses the important concepts in the single document. This model can be used to explore people interests from their writings and can serve in e-marketing.

show abstract

Word sense disambiguation

Cited by 1,405 publications

References 130 publications

Contextual Analysis of Word Meanings in Type-Theoretical Semantics

Contextual Analysis of Word Meanings in Type-Theoretical Semantics

Supervised Learning and Knowledge-Based Approaches Applied to Biomedical Word Sense Disambiguation

Road Map Approach to Automatic Topic Detection of Diaries

Contact Info

Product

Resources

About