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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Databases and Information Systems

Research Collection School Of Computing and Information Systems

2015

Computational linguistics

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Towards Opinion Summarization From Online Forums, Ding Ying, Jing Jiang Sep 2015

Towards Opinion Summarization From Online Forums, Ding Ying, Jing Jiang

Research Collection School Of Computing and Information Systems

Summarizing opinions expressed in online forums can potentially benefit many people. However, special characteristics of this problem may require changes to standard text summarization techniques. In this work, we present our initial attempt at extractive summarization of opinionated online forum threads. Given the nature of user generated content in online discussion forums, we hypothesize that besides relevance, text quality and subjectivity also play important roles in deciding which sentences are good summary sentences. We therefore construct an annotated corpus to facilitate our study of extractive summarization of online discussion forums. We define a set of features to capture relevance, text …


A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang Jul 2015

A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently out-performs a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.