A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The syntactic process
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Semantic role labeling via FrameNet, VerbNet and PropBank
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Integrating Proximity to Subjective Sentences for Blog Opinion Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Wide-coverage semantic analysis with Boxer
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
A comparative study of methods for estimating query language models with pseudo feedback
Proceedings of the 18th ACM conference on Information and knowledge management
Automatic creation of a reference corpus for political opinion mining in user-generated content
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
Proximity-based opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
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In this paper, we present natural language opinion search by unifying discourse representation structures and the subjectivity of sentences to search for relevant opinionated documents. This technique differs from existing keyword-based opinion retrieval techniques which do not consider semantic relevance of opinionated documents at discourse level. We propose a simple message model that uses the attributes of the discourse representation structures and a list of opinion words. The model compute the relevance and opinionated scores of each sentence to a given query topic. We show that the message model is able to effectively identify which entity in a sentence is directly affected by the presence of opinion words. Thus, opinionated documents containing relevant topic discourse structures are retrieved based on the instances of opinion words that directly affect the key entities in relevant sentences. In terms of MAP, experimental results show that the technique retrieves opinionated documents with better results than the standard TREC Blog 08 best run, a non-proximity technique, and a state-of-the-art proximity-based technique.