Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic

@inproceedings{Zhang2013DiscourseLE,
  title={Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic},
  author={Qi Zhang and Jin Qian and Huan Chen and Jihua Kang and Xuanjing Huang},
  booktitle={EMNLP},
  year={2013}
}
Explanatory sentences are employed to clarify reasons, details, facts, and so on. High quality online product reviews usually include not only positive or negative opinions, but also a variety of explanations of why these opinions were given. These explanations can help readers get easily comprehensible information of the discussed products and aspects. Moreover, explanatory relations can also benefit sentiment analysis applications. In this work, we focus on the task of identifying subjective… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 46 references

A PDTB-Styled End-to-End Discourse Parser

Z I H E N G L I N, H W E E T O U N G, M I N-Y E N K A N
2012
View 1 Excerpt

Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic

2012 IEEE 12th International Conference on Data Mining • 2012
View 2 Excerpts

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