scholarly journals Document Retrieval Method Using Semantic Similarity and Word Sense Disambiguation

1997 ◽  
Vol 4 (3) ◽  
pp. 51-70 ◽  
Author(s):  
KOZO OI ◽  
EIICHIRO SUMITA ◽  
HITOSHI IIDA
Keyword(s):  
Semantic Similarity ◽  
Word Sense Disambiguation ◽  
Document Retrieval ◽  
Word Sense ◽  
Retrieval Method ◽  
Sense Disambiguation

scholarly journals Arabic Gloss WSD Using BERT

2021 ◽  
Vol 11 (6) ◽  
pp. 2567
Author(s):  
Mohammed El-Razzaz ◽  
Mohamed Waleed Fakhr ◽  
Fahima A. Maghraby
Keyword(s):  
Target Word ◽  
Semantic Similarity ◽  
Test Data ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Written Text ◽  
Knowledge Based ◽  
Training Samples ◽  
Sense Disambiguation ◽  
Definition Of

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

scholarly journals Developing Corpora using Wikipedia and Word2vec for Word Sense Disambiguation

2018 ◽  
Vol 12 (3) ◽  
pp. 1239
Author(s):  
Farza Nurifan ◽  
Riyanarto Sarno ◽  
Cahyaningtyas Sekar Wahyuni
Keyword(s):  
Semantic Similarity ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Anaphora Resolution ◽  
Word Sense ◽  
Accuracy Rate ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Improve Accuracy ◽  
Research Show

Word Sense Disambiguation (WSD) is one of the most difficult problems in the artificial intelligence field or well known as AI-hard or AI-complete. A lot of problems can be solved using word sense disambiguation approaches like sentiment analysis, machine translation, search engine relevance, coherence, anaphora resolution, and inference. In this paper, we do research to solve WSD problem with two small corpora. We propose the use of Word2vec and Wikipedia to develop the corpora. After developing the corpora, we measure the sentence similarity with the corpora using cosine similarity to determine the meaning of the ambiguous word. Lastly, to improve accuracy, we use Lesk algorithms and Wu Palmer similarity to deal with problems when there is no word from a sentence in the corpora (we call it as semantic similarity). The results of our research show an 86.94% accuracy rate and the semantic similarity improve the accuracy rate by 12.96% in determining the meaning of ambiguous words.

Entity Resolution in Healthcare

2014 ◽  
pp. 385-398
Keyword(s):  
Word Sense Disambiguation ◽  
Entity Resolution ◽  
Document Retrieval ◽  
Word Sense ◽  
Sources Of Information ◽  
Linguistic Features ◽  
Language Understanding ◽  
Domain Specific ◽  
Mesh Terms ◽  
Sense Disambiguation

Abbreviations are common in biomedical documents, and many are ambiguous in the sense that they have several potential expansions. Identifying the correct expansion is necessary for language understanding and important for applications such as document retrieval. Identifying the correct expansion can be viewed as a Word Sense Disambiguation (WSD) problem. Previous approaches to resolving this problem have made use of various sources of information including linguistic features of the context in which the ambiguous term is used and domain-specific resources, such as UMLS. This chapter compares a range of knowledge sources, which have been previously used, and introduce a novel one: MeSH terms. The best performance is obtained using linguistic features in combination with MeSH terms.

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