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Extraction des caractéristiques lexico-grammaticales et couplage des unités CRF (Conditional Random Field) au réseau de neurones profond pour l'extraction des aspects

Abstract : The Internet contains a wealth of information in the form of unstructured texts such as customer comments on products, events and more. By extracting and analyzing the opinions expressed in customer comments in detail, it is possible to obtain valuable opportunities and information for customers and companies. The model proposed by Jebbara and Cimiano. for the extraction of aspects, winner of the SemEval2016 competition, suffers from the absence of lexico-grammatic input characteristics and poor performance in the detection of compound aspects. We propose the model based on a recurrent neural network for the task of extracting aspects of an entity for sentiment analysis. The proposed model is an improvement of the Jebbara and Cimiano model. The modification consists in adding a CRF to take into account the dependencies between labels and we have extended the characteristics space by adding grammatical level characteristics and lexical level characteristics. Experiments on the two SemEval2016 data sets tested our approach and showed an improvement in the F-score measurement of about 3.5%.
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https://hal.archives-ouvertes.fr/hal-02557636
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Submitted on : Monday, July 26, 2021 - 12:20:06 PM
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Saint Germes Bienvenu Bengono Obiang, Norbert Tsopze. Extraction des caractéristiques lexico-grammaticales et couplage des unités CRF (Conditional Random Field) au réseau de neurones profond pour l'extraction des aspects. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2021, Volume 33 - 2020 - Special issue CRI 2019, ⟨10.46298/arima.6438⟩. ⟨hal-02557636v4⟩

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