SemEval-2015 Task 15: A CPA dictionary-entry-building task

Baisa, Vít; Bradbury, Jane; Cinková, Silvie; Maarouf, Ismail El; Kilgarriff, Adam; Popescu, Octavian

doi:10.18653/v1/s15-2053

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…The number of semantic frames was predicted on the basis of the best value for the clustering criterion function. BOB90 4 used a supervised approach to tackle the clustering problem (Baisa et al, 2015) and get the best score on MTDSEM. An example result of FNN model on PDEV is shown in Table 2 4 Discussions…”

Section: Methodsmentioning

confidence: 99%

An End-to-end Approach to Learning Semantic Frames with Feedforward Neural Network

Feng

Xu²,

2016

Proceedings of the NAACL Student Research Workshop

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We present an end-to-end method for learning verb-specific semantic frames with feedforward neural network (FNN). Previous works in this area mainly adopt a multi-step procedure including part-of-speech tagging, dependency parsing and so on. On the contrary, our method uses a FNN model that maps verbspecific sentences directly to semantic frames. The simple model gets good results on annotated data and has a good generalization ability. Finally we get 0.82 F-score on 63 verbs and 0.73 F-score on 407 verbs.

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Section: Methodsmentioning

confidence: 99%

An End-to-end Approach to Learning Semantic Frames with Feedforward Neural Network

Feng

Xu²,

2016

Proceedings of the NAACL Student Research Workshop

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“…It is also a shared task in SemEval-2015 task 15 (Baisa et al, 2015). The task is divided into three subtasks: CPA parsing, CPA clustering and CPA lexicography.…”

Section: Cpa Experimentsmentioning

confidence: 99%

“…Corpus Pattern Analysis (CPA) is a new technique for identifying the main patterns in which a word is used in text and is currently being used to build the PDEV resource as we mentioned above. It is also a shared task in SemEval-2015 task 15 (Baisa et al, 2015). The task is divided into three subtasks: CPA parsing, CPA clustering and CPA lexicography.…”

Section: Cpa Experimentsmentioning

confidence: 99%

Semantic Frame Labeling with Target-based Neural Model

Feng

Xu³

et al. 2017

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

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This paper explores the automatic learning of distributed representations of the target's context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model's input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task harder, especially with long sentences, but also makes our model easily applicable to a range of resources and other similar tasks. We evaluate our model on several resources and get the state-of-the-art result on subtask 2 of SemEval 2015 task 15. Finally, we extend the task to word-sense disambiguation task and we also achieve a strong result in comparison to state-of-the-art work.

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“…We describe our submission to the SemEval-2015 Task 15, Subtask 1 on Corpus Pattern Analysis (Baisa et al 2015). This task is similar to semantic role labeling but with arguments based on nodes in dependency parses instead of a syntactic parse tree.…”

Section: Introductionmentioning

confidence: 99%

CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing

Mills

Levow

2015

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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We describe a system for semantic role labeling adapted to a dependency parsing framework. Verb arguments are predicted over nodes in a dependency parse tree instead of nodes in a phrase-structure parse tree. Our system participated in SemEval-2015 shared Task 15, Subtask 1: CPA parsing and achieved an Fscore of 0.516. We adapted features from prior semantic role labeling work to the dependency parsing paradigm, using a series of supervised classifiers to identify arguments of a verb and then assigning syntactic and semantic labels. We found that careful feature selection had a major impact on system performance. However, sparse training data still led rule-based systems like the baseline to be more effective than learning-based approaches.

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SemEval-2015 Task 15: A CPA dictionary-entry-building task

Cited by 3 publications

References 18 publications

An End-to-end Approach to Learning Semantic Frames with Feedforward Neural Network

An End-to-end Approach to Learning Semantic Frames with Feedforward Neural Network

Semantic Frame Labeling with Target-based Neural Model

CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing

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