WordNet and Wiktionary-Based Approach for Word Sense Disambiguation

Aouicha, Mohamed Ben; Taieb, Mohamed Ali Hadj; Marai, Hania Ibn

doi:10.1007/978-3-319-90287-6_7

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Relatedness is mostly a heuristic methodology designed by different researchers and defined based on the problem. This approach has been introduced as a novel and optimal approach by Ezzikouri et al, 2019 to improve the search for relevant information for each domain and by Aouicha et al, 2018 to address word sense disambiguation. The last approach is a hybrid methodology and combination of the mentioned approaches.…”

Section: Introductionmentioning

confidence: 99%

Geodata source retrieval by multilingual/semantic query expansion: the Case of Google Translate and WordNet version 3.1

Sajjadian

Scheider²

2022

AGILE GIScience Ser.

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Abstract. In this article, we examined the potential of the current version of WordNet and Google Translate API to enhance the quality of geodata source retrieval in the Dutch geoinformation portal (PDOK) using semantic keywords for the geographic phenomena requested. Keywords gathered from real users’ questions in natural language extracted in an English corpus. Then, these keywords were expanded using WordNet and Google Translate API. Lastly, the results of query expansion were evaluated compared to a manual gold standard and based on information retrieval metrics. Our study shows that the results of query expansion help users by reformulating good alternative queries.

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Section: Introductionmentioning

confidence: 99%

Geodata source retrieval by multilingual/semantic query expansion: the Case of Google Translate and WordNet version 3.1

Sajjadian

Scheider²

2022

AGILE GIScience Ser.

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“…In the field of Natural Language Processing (NLP), knowledge bases (KBs) play an important role in many NLP tasks. They provide rich semantic information for downstream tasks such as semantic disambiguation using WordNet's categorical information [1], bilingual embedded learning based on a multilingual KB [2]. Besides, recent researches have demonstrated that the introducing of KBs, especially commonsense KBs, not only improves the interpretability and performance of natural language processing task but also reduces the training time for machine learning [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model

Kang

Yao

et al. 2020

Applied Sciences

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Sememe is the smallest semantic unit for describing real-world concepts, which improves the interpretability and performance of Natural Language Processing (NLP). To maintain the accuracy of the sememe description, its knowledge base needs to be continuously updated, which is time-consuming and labor-intensive. Sememes predictions can assign sememes to unlabeled words and are valuable work for automatically building and/or updating sememeknowledge bases (KBs). Existing methods are overdependent on the quality of the word embedding vectors, it remains a challenge for accurate sememe prediction. To address this problem, this study proposes a novel model to improve the performance of sememe prediction by introducing synonyms. The model scores candidate sememes from synonyms by combining distances of words in embedding vector space and derives an attention-based strategy to dynamically balance two kinds of knowledge from synonymous word set and word embedding vector. A series of experiments are performed, and the results show that the proposed model has made a significant improvement in the sememe prediction accuracy. The model provides a methodological reference for commonsense KB updating and embedding of commonsense knowledge.

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WordNet and Wiktionary-Based Approach for Word Sense Disambiguation

Cited by 2 publications

References 22 publications

Geodata source retrieval by multilingual/semantic query expansion: the Case of Google Translate and WordNet version 3.1

Geodata source retrieval by multilingual/semantic query expansion: the Case of Google Translate and WordNet version 3.1

Incorporating Synonym for Lexical Sememe Prediction: An Attention-Based Model

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