Search | Engineering

Subscribe Submit

Home Journals Focus Achievement Fronts About Us 中文版

Resource Type

Journal Article 5

Year

2021 2

2018 1

2017 1

2009 1

Keywords

Question answering 2

A) sites 1

Answer selection 1

Attention 1

Deep learning 1

Knowledge memory 1

Natural order 1

PS-100HRT instrument system 1

Question and answering (Q& 1

Skip gram 1

Tagging 1

Temporality interaction 1

discovery of HRT wave precursor and its regularity 1

practicality of HRT wave model 1

“black box”& HRT wave model 1

open ︾

Search scope:

排序： Display mode:

Learning natural ordering of tags in domain-specific Q&A sites

Junfang Jia, Guoqiang Li,jiajunfang816@163.com,li.g@sjtu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 2, Pages 141-286 doi: 10.1631/FITEE.1900645

Abstract: is a defining characteristic of Web 2.0. It allows users of social computing systems (e.g., ) to use free terms to annotate content. However, is really a free action? Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community. However, there has been no work studying the regularities in how users order tags during . In this paper, we focus on the ing of tags in domain-specific Q&A sites. We study tag sequences of millions of questions in four Q&A sites, i.e., CodeProject, SegmentFault, Biostars, and CareerCup. Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions. We study the relationships between tags that can explain the emergence of ing of tags. Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the ing of tags.

Keywords： Question and answering (Q& A) sites Tagging Natural order Skip gram

HTML PDF Collect

Discovering semantically related technical terms and web resources in Q&A discussions Research Articles

Junfang Jia, Valeriia Tumanian, Guoqiang Li,li.g@sjtu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 7, Pages 969-985 doi: 10.1631/FITEE.2000186

Abstract: A sheer number of techniques and are available for software engineering practice and this number continues to grow. Discovering semantically similar or related and offers the opportunity to design appealing services to facilitate information retrieval and information discovery. In this study, we extract and from a community of question and answer (A) discussions and propose an approach based on a neural language model to learn the semantic representations of and in a joint low-dimensional vector space. Our approach maps and to a semantic vector space based only on the surrounding and of a technical term (or web resource) in a discussion thread, without the need for mining the text content of the discussion. We apply our approach to Stack Overflow data dump of March 2018. Through both quantitative and qualitative analyses in the clustering, search, and semantic reasoning tasks, we show that the learnt technical-term and web-resource vector representations can capture the semantic relatedness of and , and they can be exploited to support various search and semantic reasoning tasks, by means of simple -nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.

Keywords：技术术语；网络资源；词语嵌入；问答网站；聚类任务；推荐任务

HTML PDF Collect

Temporality-enhanced knowledgememory network for factoid question answering Article

Xin-yu DUAN, Si-liang TANG, Sheng-yu ZHANG, Yin ZHANG, Zhou ZHAO, Jian-ru XUE, Yue-ting ZHUANG, Fei WU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 104-115 doi: 10.1631/FITEE.1700788

Abstract: Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language. How to efficiently identify the exact answer with respect to a given question has become an active line of research. Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer. Most of these models suffer when a question contains very little content that is indicative of the answer. In this paper, we devise an architecture named the temporality-enhanced knowledge memory network (TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl. Unlike most of the existing approaches, our model encodes not only the content of questions and answers, but also the temporal cues in a sequence of ordered sentences which gradually remark the answer. Moreover, our model collaboratively uses external knowledge for a better understanding of a given question. The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.

Keywords： Question answering Knowledge memory Temporality interaction

HTML PDF Collect

Attention-based encoder-decoder model for answer selection in question answering Article

Yuan-ping NIE, Yi HAN, Jiu-ming HUANG, Bo JIAO, Ai-ping LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4, Pages 535-544 doi: 10.1631/FITEE.1601232

Abstract: One of the key challenges for question answering is to bridge the lexical gap between questions and answers because there may not be any matching word between them. Machine translation models have been shown to boost the performance of solving the lexical gap problem between question-answer pairs. In this paper, we introduce an attention-based deep learning model to address the answer selection task for question answering. The proposed model employs a bidirectional long short-term memory (LSTM) encoder-decoder, which has been demonstrated to be effective on machine translation tasks to bridge the lexical gap between questions and answers. Our model also uses a step attention mechanism which allows the question to focus on a certain part of the candidate answer. Finally, we evaluate our model using a benchmark dataset and the results show that our approach outperforms the existing approaches. Integrating our model significantly improves the performance of our question answering system in the TREC 2015 LiveQA task.

Keywords： Question answering Answer selection Attention Deep learning

HTML PDF Collect

The feasibility of accurate earthquake prediction by HRT wave and examples of strong earthquakes including Wenchuan Earthquake （M ＝8）

Zhao Yulin and Qian Fuye

Strategic Study of CAE 2009, Volume 11, Issue 6, Pages 111-122

Abstract:

The method of earthquake short-term and impending prediction by using tidal wave (abbr HRTwave method) is a new, quantitative technology of simultaneous determination of the location, magnitude and time of an earthquake.The earthquake preparation processes and precursor mechanism are considered as a "blackbox" .Using the modern control theory in system recognition, we can calibrate this "black box" mathematically with different input and output parameters.We regard the tidal force as our "input signal" while geoelectric response to the tidal forces is considered as an "output" .

Keywords： “black box”& HRT wave model PS-100HRT instrument system discovery of HRT wave precursor and its regularity practicality of HRT wave model