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Italian Sense Inventory
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Poli, Francesca. - : Università di Pisa, 2021. : Istituto di Linguistica Computazionale “A. Zampolli” - Consiglio Nazionale delle Ricerche (ILC-CNR), 2021
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Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet
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In: Electronics; Volume 10; Issue 23; Pages: 2938 (2021)
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A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain
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In: Information ; Volume 12 ; Issue 11 (2021)
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NERWS: Towards Improving Information Retrieval of Digital Library Management System Using Named Entity Recognition and Word Sense
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In: Big Data and Cognitive Computing ; Volume 5 ; Issue 4 (2021)
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Abstract:
An information retrieval (IR) system is the core of many applications, including digital library management systems (DLMS). The IR-based DLMS depends on either the title with keywords or content as symbolic strings. In contrast, it ignores the meaning of the content or what it indicates. Many researchers tried to improve IR systems either using the named entity recognition (NER) technique or the words’ meaning (word sense) and implemented the improvements with a specific language. However, they did not test the IR system using NER and word sense disambiguation together to study the behavior of this system in the presence of these techniques. This paper aims to improve the information retrieval system used by the DLMS by adding the NER and word sense disambiguation (WSD) together for the English and Arabic languages. For NER, a voting technique was used among three completely different classifiers: rules-based, conditional random field (CRF), and bidirectional LSTM-CNN. For WSD, an examples-based method was used to implement it for the first time with the English language. For the IR system, a vector space model (VSM) was used to test the information retrieval system, and it was tested on samples from the library of the University of Kufa for the Arabic and English languages. The overall system results show that the precision, recall, and F-measures were increased from 70.9%, 74.2%, and 72.5% to 89.7%, 91.5%, and 90.6% for the English language and from 66.3%, 69.7%, and 68.0% to 89.3%, 87.1%, and 88.2% for the Arabic language.
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Keyword:
digital library management system; information retrieval system; named entity recognition; word sense disambiguation
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URL: https://doi.org/10.3390/bdcc5040059
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FlauBERT: Unsupervised Language Model Pre-training for French
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In: Proceedings of the 12th Language Resources and Evaluation Conference ; LREC ; https://hal.archives-ouvertes.fr/hal-02890258 ; LREC, 2020, Marseille, France (2020)
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FlauBERT : Unsupervised Language Model Pre-training for French ; FlauBERT : des modèles de langue contextualisés pré-entraînés pour le français
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In: Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-02784776 ; 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 2 : Traitement Automatique des Langues Naturelles, Jun 2020, Nancy, France. pp.268-278 (2020)
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An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems ...
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An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems ...
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An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems ...
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Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet
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In: Future Internet ; Volume 11 ; Issue 5 (2019)
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Knowledge-Based Method for Word Sense Disambiguation by Using Hindi WordNet ...
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Knowledge-Based Method for Word Sense Disambiguation by Using Hindi WordNet ...
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RUSSE'2018: Human-Annotated Sense-Disambiguated Word Contexts for Russian ...
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RUSSE'2018: Human-Annotated Sense-Disambiguated Word Contexts for Russian ...
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Crowdsourcing lexical semantic judgements from bilingual dictionary users
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A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model
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In: Entropy ; Volume 17 ; Issue 9 ; Pages 6007-6024 (2015)
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Dynamic topic adaptation for improved contextual modelling in statistical machine translation
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Methods for open information extraction and sense disambiguation on natural language text ; Methoden der Offenen Informationsextraktion und Bedeutungsdisambiguierung in Texten
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