3,059 research outputs found
Analysis and Detection of Information Types of Open Source Software Issue Discussions
Most modern Issue Tracking Systems (ITSs) for open source software (OSS)
projects allow users to add comments to issues. Over time, these comments
accumulate into discussion threads embedded with rich information about the
software project, which can potentially satisfy the diverse needs of OSS
stakeholders. However, discovering and retrieving relevant information from the
discussion threads is a challenging task, especially when the discussions are
lengthy and the number of issues in ITSs are vast. In this paper, we address
this challenge by identifying the information types presented in OSS issue
discussions. Through qualitative content analysis of 15 complex issue threads
across three projects hosted on GitHub, we uncovered 16 information types and
created a labeled corpus containing 4656 sentences. Our investigation of
supervised, automated classification techniques indicated that, when prior
knowledge about the issue is available, Random Forest can effectively detect
most sentence types using conversational features such as the sentence length
and its position. When classifying sentences from new issues, Logistic
Regression can yield satisfactory performance using textual features for
certain information types, while falling short on others. Our work represents a
nontrivial first step towards tools and techniques for identifying and
obtaining the rich information recorded in the ITSs to support various software
engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering
(ICSE2019
Joint Modeling of Content and Discourse Relations in Dialogues
We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show that our joint model can outperform state-of-the-art approaches
for both phrase-based content selection and discourse relation prediction
tasks. We also evaluate our model on predicting the consistency among team
members' understanding of their group decisions. Classifiers trained with
features constructed from our model achieve significant better predictive
performance than the state-of-the-art.Comment: Accepted by ACL 2017. 11 page
Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier
With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems,
they often tend to generate factually inconsistent text with respect to their
input. This phenomenon is emphasized in tasks like summarization, in which the
generated summaries should be corroborated by their source article. In this
work, we leverage recent progress on textual entailment models to directly
address this problem for abstractive summarization systems. We use
reinforcement learning with reference-free, textual entailment rewards to
optimize for factual consistency and explore the ensuing trade-offs, as
improved consistency may come at the cost of less informative or more
extractive summaries. Our results, according to both automatic metrics and
human evaluation, show that our method considerably improves the faithfulness,
salience, and conciseness of the generated summaries.Comment: ACL 202
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