Automatic labeling of semantic roles
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SWAT-MP: the SemEval-2007 systems for task 5 and task 14
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
JU_CSE_TEMP: A first step towards evaluating events, time expressions and temporal relations
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Roles of event actors and sentiment holders in identifying event-sentiment association
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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In this paper, we have identified event and sentiment expressions at word level from the sentences of TempEval-2010 corpus and evaluated their association in terms of lexical equivalence and co-reference. A hybrid approach that consists of Conditional Random Field (CRF) based machine learning framework in conjunction with several rule based strategies has been adopted for event identification within the TimeML framework. The strategies are based on semantic role labeling, WordNet relations and some handcrafted rules. The sentiment expressions are identified simply based on the cues that are available in the sentiment lexicons such as Subjectivity Wordlist, SentiWordNet and WordNet Affect. The identification of lexical equivalence between event and sentiment expressions based on the part-of-speech (POS) categories is straightforward. The emotional verbs from VerbNet have also been employed to improve the coverage of lexical equivalence. On the other hand, the association of sentiment and event has been analyzed using the notion of co-reference. The parsed dependency relations along with basic rhetoric knowledge help to identify the co-reference between event and sentiment expressions. Manual evaluation on the 171 sentences of TempEval-2010 dataset yields the precision, recall and F-Score values of 61.25%, 70.29% and 65.23% respectively.