Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering

Esposito, Massimo; Damiano, Emanuele; Minutolo, Aniello; Pietro, Giuseppe De; Fujita, Hamido

doi:10.1016/j.ins.2019.12.002

Cited by 92 publications

(31 citation statements)

References 26 publications

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“…The question classification plays a vital role in extracting the correct answer in the Question Answering System. The method proposed by Esposito et al, [14] extracts the most relevant terms from the questions, and then these words are placed in the context. This document collection is later used in the QA system.…”

Section: Related Workmentioning

confidence: 99%

Automatic question-answer pairs generation and question similarity mechanism in question answering system

2021

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With the swift growth of the information over the past few years, taking full benefit is increasingly essential. Question Answering System is one of the promising methods to access this much information. The Question Answering System lacks humans’ common sense and reasoning power and cannot identify unanswerable questions and irrelevant questions. These questions are answered by making unreliable and incorrect guesses. In this paper, we address this limitation by proposing a Question Similarity mechanism. Before a question is posed to a Question-Answering system, it is compared with possible generated questions of the given paragraph, and then a Question Similarity Score is generated. The Question Similarity mechanism effectively identifies the unanswerable and irrelevant questions. The proposed Question Similarity mechanism incorporates a human way of reasoning to identify unanswerable and irrelevant questions. This mechanism can avoid the unanswerable and irrelevant questions altogether from being posed to the Question Answering system. It helps the Question Answering Systems to focus only on the answerable questions to improve their performance. Along with this, we introduce an application of the Question Answering System that generates the question-answer pairs given a passage and is useful in several fields.

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Section: Related Workmentioning

confidence: 99%

Automatic question-answer pairs generation and question similarity mechanism in question answering system

2021

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“…is paper also presents a parameter b to the weight of the central word attending itself, which can be calculated as in equation (6).…”

Section: Gaussian Attention Weightmentioning

confidence: 99%

“…Recently, the emergence of word embedding techniques [5], which map a word into a numerical vector, results in many methods achieving success via sentence embeddings in textual similarity tasks [6,7]. For example, Tien et al [8] encoded many features from various sets of word embeddings into one embedding and secondly learned similarity between sentences via the new embedding.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Luo

Shan

Zhang

et al. 2021

Mathematical Problems in Engineering

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The textual similarity task, which measures the similarity between two text pieces, has recently received much attention in the natural language processing (NLP) domain. However, due to the vagueness and diversity of language expression, only considering semantic or syntactic features, respectively, may cause the loss of critical textual knowledge. This paper proposes a new type of structure tree for sentence representation, which exploits both syntactic (structural) and semantic information known as the weight vector dependency tree (WVD-tree). WVD-tree comprises structure trees with syntactic information along with word vectors representing semantic information of the sentences. Further, Gaussian attention weight is proposed for better capturing important semantic features of sentences. Meanwhile, we design an enhanced tree kernel to calculate the common parts between two structures for similarity judgment. Finally, WVD-tree is tested on widely used semantic textual similarity tasks. The experimental results prove that WVD-tree can effectively improve the accuracy of sentence similarity judgments.

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“…With the wide employment of embedding methods in various machine learning tasks [31,32,33,34,35], network embedding also gains more and more attentions and applications [36,37]. Network embedding refers to assigning nodes in a network to low-dimensional representations and effectively preserving the network structure [36].…”

Section: Network Embeddingmentioning

confidence: 99%

Deep Collaborative Embedding for information cascade prediction

Zhao

Yang

Lin

et al. 2020

Knowledge-Based Systems

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Recently, information cascade prediction has attracted increasing interest from researchers, but it is far from being well solved partly due to the three defects of the existing works. First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion. Second, the existing works often ignore the prediction of the infection order, which also plays an important role in social network analysis. At last, the existing works often depend on the requirement of underlying diffusion networks which are likely unobservable in practice. In this paper, we aim at the prediction of both node infection and infection order without requirement of the knowledge about the underlying diffusion mechanism and the diffusion network, where the challenges are two-fold. The first is what cascading characteristics of nodes should be captured and how to capture them, and the second is that how to model the non-linear features of nodes in information cascades. To address these challenges, we propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction, which can capture not only the node structural property but also two kinds of node cascading characteristics. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration, in which way the non-linearity of information cascades * Ning Yang and Tao Lin share the corresponding authorship.can be effectively captured. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.

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Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering

Cited by 92 publications

References 26 publications

Automatic question-answer pairs generation and question similarity mechanism in question answering system

Automatic question-answer pairs generation and question similarity mechanism in question answering system

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Deep Collaborative Embedding for information cascade prediction

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