Leveraging linguistic traits and semi-supervised learning to single out informational content across how-to community question-answering archives

Palomera, Daniel; Figueroa, Alejandro

doi:10.1016/j.ins.2016.11.006

Cited by 26 publications

(14 citation statements)

References 17 publications

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“…By means of automatically generating equivalent question patterns, the model achieved over 57% recall and over 54% precision, but still did not completely solve the defects of question and answer matching. After a long period of research, Palmera and Figueroa (2017) [25] matched the intention of the newly published question with the intention of the archived answer presented to the questioner. By manually annotating the number of how-to questions and answers, the accuracy of the best answer retrieval was increased by 4.12%.…”

Section: A Chinese Restricted Domains and Question Pair Matchingmentioning

confidence: 98%

“…By manually annotating the number of how-to questions and answers, the accuracy of the best answer retrieval was increased by 4.12%. Inspired by Palmera and Figueroa [25], in this paper, we used the method of question pair matching to solve the difficulty of the large number and great change of external input questions of Chinese restricted domain in the professional field, and significantly improved corresponding accuracy.…”

Section: A Chinese Restricted Domains and Question Pair Matchingmentioning

confidence: 99%

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Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

Cai¹,

Min²,

Sitong³

et al. 2020

IEEE Access

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With the rapid expansion of the Internet, intelligent question answering for information retrieval has once again gained widespread attention. However, current question answering models mainly focus on the general and common-sense questions in open domains and are incapable to effectively solve more complex professional domain questions. This paper proposed an integrated framework for Chinese intelligent question answering in restricted domains. The proposed model fused convolutional neural network and bidirectional long short-term memory network which performs efficient semantic analysis on the question pairs to extract more effective features of the text. Meanwhile, the coattention mechanism and attention mechanism were combined to obtain the semantic interaction and feature representation of the question pair for providing complete information for subsequent calculations. In addition, we introduced the method of question pair matching to implement the Chinese intelligent question answering in a restricted domain. Experiments were tested and evaluated on the open-source CCKS2018 dataset and our private self-built inverted pendulum control question answering (IPC-QA) dataset for automation control virtual learning environment. Experimental results confirm that the proposed models are efficient and achieve a high precision of 0.86042 and 0.8031 on CCKS2018 and IPC-QA respectively.

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Section: A Chinese Restricted Domains and Question Pair Matchingmentioning

confidence: 98%

Section: A Chinese Restricted Domains and Question Pair Matchingmentioning

confidence: 99%

Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

Cai¹,

Min²,

Sitong³

et al. 2020

IEEE Access

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show abstract

“…Image [7], [8], [9], [10], [11], [12], [13] Health [2], [1], [14], [15] Music/ Movie [3], [16], [17] Web Search/Social Media [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43] General [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54],…”

Section: Table 3 Distribution Of Fields Area Of Intent Diversity Infmentioning

confidence: 99%

“…It explains models of intent diversity approaches which used in the information retrieval field. The model distribution of intent diversity in information retrieval seen in [1], [49], [37], [62], [39], [43].…”

Section: Fig 4 Distribution Of Intent Diversity Approach Papersmentioning

confidence: 99%

Review of Intent Diversity in Information Retrieval : Approaches, Models and Trends

Mustakim¹,

Wardoyo²,

Mustofa³

2018

JITeCS

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The fast increasing volume of information databases made some difficulties for a user to find the information that they need. Its important for researchers to find the best method for challenging this problem. user intention detection can be used to increase the relevancies of information delivered from the information retrieval system. This research used a systematic mapping process to identify what area, approaches, and models that mostly used to detect user intention in information retrieval in four years later. the result of this research identified that item-based approach is still the most approach researched by researchers to identify intent diversity in information retrieval. The used of item-based approach still increasing from 2015 until 2017. 34% paper used topic models in their research. It means that Topic models still the necessary models explored by the researchers in this study.

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“…First of all, mapping question-answer pairs into a discriminative feature space is a critical step. A widely adopted approach is to encode question-answer pairs using various features, e.g., lexical, linguistic, and syntactic features [Zou et al, 2015, Petrosyan et al, 2015, Toba et al, 2014, Yen et al, 2013, Palomera and Figueroa, 2017 Figure 5.2: Overview of QDLinker. It directly links programmer's question to formal documentation through embedding the semantic context in CQA.…”

Section: Background and Motivationmentioning

confidence: 99%

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Li¹

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Leveraging linguistic traits and semi-supervised learning to single out informational content across how-to community question-answering archives

Cited by 26 publications

References 17 publications

Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

Review of Intent Diversity in Information Retrieval : Approaches, Models and Trends

Supporting information needs of developers through web Q&A discussions

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