Automatic labeling of semantic roles
Computational Linguistics
Representing Discourse Coherence: A Corpus-Based Study
Computational Linguistics
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion holder extraction from author and authority viewpoints
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying Opinion Holders in Opinion Text from Online Newspapers
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Temporal analysis of sentiment events: a visual realization and tracking
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Identifying event: sentiment association using lexical equivalence and co-reference approaches
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Emotion holder for emotional verbs – the role of subject and syntax
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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In this paper, we study the roles of event actors and sentiment holders from the perspective of event sentiment relations within the TimeML framework. The proposed algorithm is bootstrapping in nature that identifies the association between the event and sentiment expressions. There are two basic steps of the algorithm and they deal with lexical keyword spotting and co-reference resolution. We consider the associations between the event and sentiment expressions that are in the same or different text segments. Guided by the classical definitions of events in the TempEval-2 shared task, a manual evaluation is attempted to distinguish the sentiment events from the factual events and the agreement was satisfactory. In order to computationally estimate the different sentiments associated with different events, the knowledge of event actors and sentiment holders is introduced. To identify the roles between the event actors and sentiment holders, appropriate method is proposed. From the experiments, it is observed that the lexical equivalence between event and sentiment expressions easily identifies the similar entities that are both responsible for the event actors and sentiment holders. If the event and sentiment expressions occupy different text segments, the identification of their corresponding event actors and sentiment holders needs the knowledge of parsed-dependency relations, named entities along with the anaphors. The manual evaluation produces satisfactory results on the test documents of the TempEval-2 shared task in case of identifying the many to many associations between the event actors and sentiment holders for a specific event.