Enhancing Question Retrieval in Community Question Answering Using Word Embeddings

Othman, Nouha; Faiz, Rim; Smaïli, Kamel

doi:10.1016/j.procs.2019.09.203

Cited by 23 publications

(12 citation statements)

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“… WEKOS [20]: A word embedding based method which transforms words in each question into continuous vectors. The questions are clustered using Kmeans and the similarity between them was measured using cosine similarity based on their weighted continuous valued vectors.…”

Section: Resultsmentioning

confidence: 99%

“…The use of word embeddings allows to effectively detect the syntactic and semantic similarities between words. Particularly, we resorted to the Continuous Bag-of-Words (CBOW) model which has proven to outperform Skip gram on our datasets [19]. Recall that CBOW consists in estimating a pivot word according to its context using a window of contextual words around it, while Skip gram does the inverse predicting the contextual words given a current word in a sliding window.…”

Section: Word Embedding Learningmentioning

confidence: 99%

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Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Othman

Faiz

Smaïli

2022

Data & Knowledge Engineering

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In this paper, we tackle the task of similar question retrieval (QR) which is essential for Commu-nity Question Answering (cQA) and aims to retrieve historical questions that are semantically equivalent to the new queries. Over time, with the sharp increase of community archives and the accumulation of duplicated questions, the QR problem has become increasingly challenging due to the shortness of the community questions as well as the word mismatch problem as users can formulate the same query using different wording. Although many efforts have been devoted to address this problem, existing methods mostly relied on supervised models which significantly depend on massive training data sets and manual feature engineering. Such methods are chiefly constrained by their specificities that ignore the word order and do not capture enough syntactic and semantic information in questions. In this paper, we rely on Neural Networks (NNs) which use a deep analysis of words and questions to take into consideration the semantics as well as the structure of questions to predict the semantic text similarity. We propose a deep learning approach based on a Siamese architecture with Long Short-Term Memory (LSTM) networks, augmented with an attention mechanism to let the model give different words different attention while modeling questions. We also explore the use of Convolutional Neural Networks (CNN) nested within the Siamese architecture to retrieve relevant questions. Different similarity measures were tested to predict the semantic similarity between the the pairs of questions. To evaluate the proposed approach, we conducted experiments on large-scale datasets in English and Arabic.

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Section: Resultsmentioning

confidence: 99%

Section: Word Embedding Learningmentioning

confidence: 99%

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Othman

Faiz

Smaïli

2022

Data & Knowledge Engineering

Self Cite

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show abstract

“…-WEKOS [14]: A word embedding based method which transforms words in each question into continuous vectors. The questions are clustered using Kmeans and the similarity between them was measured using the cosine similarity based on their weighted continuous valued vectors.…”

Section: Main Results and Discussionmentioning

confidence: 99%

Manhattan Siamese LSTM for Question Retrieval in Community Question Answering

Othman¹,

Faiz

Smaïli

2019

Lecture Notes in Computer Science

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Community Question Answering (cQA) are platforms where users can post their questions, expecting for other users to provide them with answers. We focus on the task of question retrieval in cQA which aims to retrieve previous questions that are similar to new queries. The past answers related to the similar questions can be therefore used to respond to the new queries. The major challenges in this task are the shortness of the questions and the word mismatch problem as users can formulate the same query using different wording. Although question retrieval has been widely studied over the years, it has received less attention in Arabic and still requires a non trivial endeavour. In this paper, we focus on this task both in Arabic and English. We propose to use word embeddings, which can capture semantic and syntactic information from contexts, to vectorize the questions. In order to get longer sequences, questions are expanded with words having close word vectors. The embedding vectors are fed into the Siamese LSTM model to consider the global context of questions. The similarity between the questions is measured using the Manhattan distance. Experiments on real world Yahoo! Answers dataset show the efficiency of the method in Arabic and English.

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“…Song et al [6] proposed text matching models like triple CNN and two attention based triple CNN models for improving IR based QA system for e-commerce-AliMe. The usage of word embeddings that captures semantic information from contexts for question vectorization was explained by Othman et al [7]. QA system for evaluated medical questions was described by Abacha and Demner-Fushman [8] which recognizes question entailment.…”

Section: Related Workmentioning

confidence: 99%

Query-Based Retrieval Using Universal Sentence Encoder

Deepthi¹,

Sowjanya²

2021

RIA

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In Natural language processing, various tasks can be implemented with the features provided by word embeddings. But for obtaining embeddings for larger chunks like sentences, the efforts applied through word embeddings will not be sufficient. To resolve such issues sentence embeddings can be used. In sentence embeddings, complete sentences along with their semantic information are represented as vectors so that the machine finds it easy to understand the context. In this paper, we propose a Question Answering System (QAS) based on sentence embeddings. Our goal is to obtain the text from the provided context for a user-query by extracting the sentence in which the correct answer is present. Traditionally, infersent models have been used on SQUAD for building QAS. In recent times, Universal Sentence Encoder with USECNN and USETrans have been developed. In this paper, we have used another variant of the Universal sentence encoder, i.e. Deep averaging network in order to obtain pre-trained sentence embeddings. The results on the SQUAD-2.0 dataset indicate our approach (USE with DAN) performs well compared to Facebook’s infersent embedding.

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Enhancing Question Retrieval in Community Question Answering Using Word Embeddings

Cited by 23 publications

References 1 publication

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Learning English and Arabic question similarity with Siamese Neural Networks in community question answering services

Manhattan Siamese LSTM for Question Retrieval in Community Question Answering

Query-Based Retrieval Using Universal Sentence Encoder

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