Enhancing Question Retrieval in Community Question Answering Using Word Embeddings - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

Enhancing Question Retrieval in Community Question Answering Using Word Embeddings

Résumé

Community Question Answering (CQA) services have evolved into a popular way of online information seeking, where users can interact and exchange knowledge in the form of questions and answers. In this paper, we study the problem of finding historical questions that are semantically equivalent to the queried ones, assuming that the answers to the similar questions should also answer the new ones. The major challenge of question retrieval is the word mismatch problem between questions, as users can formulate the same question using different wording. Most existing methods measure the similarity between questions based on the bag-of-words (BOWs) representation capturing no semantics between words. Therefore, this study proposes to use word embeddings, which can capture semantic and syntactic information from contexts, to vectorize the questions. The questions are clustered using Kmeans to speed up the search and ranking tasks. The similarity between the questions is measured using cosine similarity based on their weighted continuous valued vectors. We run our experiments on real world data set from Yahoo! Answers in English and Arabic to show the efficiency and generality of our proposed method.
Fichier principal
Vignette du fichier
KES2019_paper.pdf (381.82 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02126846 , version 1 (13-05-2019)

Identifiants

  • HAL Id : hal-02126846 , version 1

Citer

Nouha Othman, Rim Faiz, Kamel Smaïli. Enhancing Question Retrieval in Community Question Answering Using Word Embeddings. Enhancing Question Retrieval in Community Question Answering Using Word Embeddings: Proceedings of the 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems KES2019, Sep 2019, Budapest, Hungary. ⟨hal-02126846⟩
119 Consultations
384 Téléchargements

Partager

Gmail Facebook X LinkedIn More