Evaluation of the proposed approach on TimeBank corpus shows that sequential modeling is capable of accurately recognizing temporal relations between events, which outperforms a neural net model using various discrete features as input that imitates previous feature based models.",Ī Sequential Model for Classifying Temporal Relations between Intra-Sentence Events The neural nets learn compositional syntactic and semantic representations of contexts surrounding the two events and predict the temporal relation between them. The context word sequence, together with a parts-of-speech tag sequence and a dependency relation sequence that are generated corresponding to the word sequence, are then provided as input to bidirectional recurrent neural network (LSTM) models. Specifically, our approach first extracts a sequence of context words that indicates the temporal relation between two events, which well align with the dependency path between two event mentions. The key observation is that the overall syntactic structure and compositional meanings of the multi-word context between events are important for distinguishing among fine-grained temporal relations. Publisher = "Association for Computational Linguistics",Ībstract = "We present a sequential model for temporal relation classification between intra-sentence events.
Sequential model mods#
Cite (Informal): A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events (Choubey & Huang, EMNLP 2017) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events",īooktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", Association for Computational Linguistics. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1796–1802, Copenhagen, Denmark. A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events. Anthology ID: D17-1190 Volume: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing Month: September Year: 2017 Address: Copenhagen, Denmark Venue: EMNLP SIG: SIGDAT Publisher: Association for Computational Linguistics Note: Pages: 1796–1802 Language: URL: DOI: 10.18653/v1/D17-1190 Bibkey: choubey-huang-2017-sequential Cite (ACL): Prafulla Kumar Choubey and Ruihong Huang. Evaluation of the proposed approach on TimeBank corpus shows that sequential modeling is capable of accurately recognizing temporal relations between events, which outperforms a neural net model using various discrete features as input that imitates previous feature based models.
Abstract We present a sequential model for temporal relation classification between intra-sentence events.
0 kommentar(er)
