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Weakly-Supervised Opinion Summarization by Leveraging External Information
Opinion summarization from online product reviews is a challenging task,...
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Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
Product-related question answering (QA) is an important but challenging ...
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A study on the relation between linguistics-oriented and domain-specific semantics
In this paper we dealt with the comparison and linking between lexical r...
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Draw This Object: A Study of Debugging Representations
Domain-specific debugging visualizations try to provide a view of a runt...
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Analogy Mining for Specific Design Needs
Finding analogical inspirations in distant domains is a powerful way of ...
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Supervised Complementary Entity Recognition with Augmented Key-value Pairs of Knowledge
Extracting opinion targets is an important task in sentiment analysis on...
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NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining
In this paper, we present neural model architecture submitted to the Sem...
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A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining
Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach.
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