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Calculating semantic relatedness of lists of nouns using WordNet path length ...
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Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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Capability Language Processing (CLP): Classification and Ranking of Manufacturing Suppliers Based on Unstructured Capability Data
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TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing
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Easy-to-use combination of POS and BERT model for domain-specific and misspelled terms
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In: NL4IA Workshop Proceedings ; https://hal.archives-ouvertes.fr/hal-03474696 ; NL4IA Workshop Proceedings, Nov 2021, Milan, Italy (2021)
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Investigating the impact of preprocessing on document embedding: an empirical comparison
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In: ISSN: 1759-1163 ; EISSN: 1759-1171 ; International Journal of Data Mining, Modelling and Management ; https://hal.inrae.fr/hal-03574696 ; International Journal of Data Mining, Modelling and Management, Inderscience, 2021, 13 (4), pp.351-363 (2021)
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Avoiding gender ambiguous pronouns in French
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-03374279 ; Cognition, Elsevier, 2021 (2021)
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eXSTS: eXplainable Semantic Textual Similarity
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Abstract:
eXSTS comprises two parts, the back-end data analysis system and the front-end user interface (UI). The back-end system consists of a Siamese Neural Network (SNN) and BERT\textsubscript{base} pre-trained model. eXSTS receives two documents, and the back-end system splits two documents into sentences, defines pairs of sentences across the two documents, and calculates the STS score of each sentence pair by comparing the sentence embeddings of the two sentences. We normalize all the STS scores and calculate the document STS score of these two documents. The front-end UI visualizes the STS information of sentence pairs through proposed visualizations to explain the sentence pairs that significantly affect the document STS score of two documents and the global distribution of the STS scores of all the sentences pairs across two documents. ; We propose eXplainable Semantic Textual Similarity (eXSTS) with two visualizations that allow the users to investigate the Semantic Textual Similarity (STS) relationship between two documents. eXSTS offers insights for the users who are not familiar with Natural Language Processing to the STS relationship between two documents. eXSTS was invented to deal with the job advertisement classification task. When a job advertisement is entered into eXSTS, eXSTS retrieves the five most relevant National Occupational Classification (NOC) unit group based on the document STS score of the job advertisement and each NOC unit group. The front-end user interface demonstrates the STS relationship between the job advertisement and one NOC unit group in the five most relevant NOC unit groups to explore the important STS information across the job advertisement and this NOC unit group and why eXSTS chose this NOC unit group to opt-in the five most relevant NOC unit groups.
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Keyword:
Human-computer interaction; Information Retrieval; Job advertisement classification; Semantic Textual Similarity; Visualization
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URL: http://hdl.handle.net/10222/80763
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Evaluation of Sentence Representations in Semantic Text Similarity Tasks ; Utvärdering av meningsrepresentation för semantisk textlikhet
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Avoiding conflict : when speaker coordination does not require conceptual agreement
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In: ISSN: 2624-8212 ; Frontiers in Artificial Intelligence, Vol. 3, No 523920 (2021) P. 24 (2021)
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Encoding interference effects support self-organized sentence processing
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In: ISSN: 0010-0285 ; Cognitive Psychology, Vol. 124 (2021) P. 101356 (2021)
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Semantic Oppositeness for Inconsistency and Disagreement Detection in Natural Language
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Semantically Meaningful Sentence Embeddings
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In: Computer Science and Engineering: Theses, Dissertations, and Student Research (2021)
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Thematic similarity real-time computation during an online debate ; Recherche de similarité thématique en temps réel au sein d'un débat en ligne
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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 ; 27e édition du Traitement Automatique des Langues Naturelles (TALN) ; https://hal.archives-ouvertes.fr/hal-02784775 ; 27e édition du Traitement Automatique des Langues Naturelles (TALN), Jun 2020, Nancy, France. pp.258-267 ; https://jep-taln2020.loria.fr/ (2020)
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Natural language understanding in argumentative dialogue systems ...
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Towards automatic linking of lexicographic data: the case of a historical and a modern Danish dictionary
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Investigating the Portuguese-English Bilingual Mental Lexicon: Crosslinguistic Orthographic and Phonological Overlap in Cognates and False Friends ...
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Improving Sentence Representations via Component Focusing
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In: Applied Sciences ; Volume 10 ; Issue 3 (2020)
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Corpus-Based Paraphrase Detection Experiments and Review
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In: Information; Volume 11; Issue 5; Pages: 241 (2020)
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