semanticsdriven shallow parsing for chinese semantic role labeling

... Shallow semantc parsing of Chinese. In Daniel Marcu Susan Dumais and Salim Roukos, editors, HLT- NAACL 2004: Main Proceedings. Weiwei Sun. 2010. Semantics-driven shallow parsing for Chinese semantic ... preliminary linguistic explanations for the phenomena. 2 Chinese SRL The Chinese PropBank (CPB) is a semantic anno- tation for the syntactic trees of the Chinese Tree- Bank (CTB). The arguments ... argument. For the two classification prob- lems, parsing can provide complex syntactic infor- mation such as path features. 4.3.1 The Effect of Parsing in AI In AI, full parsing is very important for...

Ngày tải lên: 20/02/2014, 04:20

... syntactic parsing and inference in semantic role labeling. Comput. Linguist. Kristina Toutanova, Aria Haghighi, and Christopher D. Manning. 2008. A global joint model for semantic role labeling. ... Automatic labeling of semantic roles. Computational Linguis- tics, 28:245–288. Peter Koomen, Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2005. Generalized inference with multiple semantic role labeling ... relations among semantic roles mentioned and can be simplified to: Agent  Instrument  Patient/Theme Emerging from a range of more basic semantic properties of the ranked semantic roles, thematic hierarchies...

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... Association for Computational Linguistics A Hybrid Convolution Tree Kernel for Semantic Role Labeling Wanxiang Che Harbin Inst. of Tech. Harbin, China, 150001 car@ir.hit.edu.cn Min Zhang Inst. for Infocomm ... particular, for each target verb (predicate) all the constituents in the sentence which fill a semantic role (argument) of the verb have to be recognized. Figure 1 shows an example of a semantic role labeling ... shared task: Semantic role labeling. In Proceedings of CoNLL-2004, pages 89– 97. Xavier Carreras and Llu ´ ıs M ` arquez. 2005. Introduc- tion to the CoNLL-2005 shared task: Semantic role labeling. ...

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... and Jurafsky, D. 2002. Automatic labeling of semantic roles. CL, 28(3):245-288. He, S. and Gildea, H. 2006. Self-training and Co- training for Semantic Role Labeling: Primary Re- port. TR 891, ... application of self-training in learning semantic role labeling with the use of unlabeled data. We used a balancing method for selecting newly labeled examples for augmenting the training set in ... Investigation of Weakly Supervised Learning for Semantic Role Labeling. In Proceedings of the Sixth international Conference on Advanced Language Processing and Web information Technology (ALPIT 2007),...

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... pronoun and number. Similarly to Table 5, the semantic role of the anaphor ranks higher than the one of the antecedent. This re- 145 Semantic Role Labeling for Coreference Resolution Simone Paolo Ponzetto ... explored tech- niques for robust, broad coverage semantic pars- ing in terms of semantic role labeling (Gildea & Jurafsky, 2002; Carreras & M ` arquez, 2005, SRL henceforth). This paper ... annotated corpus of semantic roles. Com- putational Linguistics, 31(1):71–105. Pradhan, S., W. Ward, K. Hacioglu, J. H. Martin & D. Juraf- sky (2004). Shallow semantic parsing using support...

Ngày tải lên: 08/03/2014, 21:20

... and Martha Palmer, 2004. Calibrating Features for Semantic Role Labeling. EMNLP ’04. Nianwen Xue, 2008. Labeling Chinese Predicates with Semantic Roles. Computational Linguistics, 34(2):225–255. 36 ... Intro- duction to the CoNLL–2004 Shared Task: Semantic Role Labeling. CoNLL ’04. 35 to identify multi-word verbs, and therefore do not report the model’s performance in identifying verb boundaries. Since ... EMNLP ’06. Kadri Hacioglu, 2004. Semantic Role Labeling using Dependency Trees. COLING ’04. Kadri Hacioglu and Wayne Ward, 2003. Target Word Detection and Semantic Role Chunking using Sup- port...

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... Word- Net for robust semantic parsing. In Proceedings of Cicling 2005, Mexico. Cynthia A. Thompson, Roger Levy, and Christopher Manning. 2003. A generative model for semantic role labeling. In ... for shallow semantic parsing. In ACL04, Barcelona, Spain. Sameer Pradhan, Kadri Hacioglu, Valeri Krugler, Wayne Ward, James H. Martin, and Daniel Jurafsky. 2005. Support vector learning for semantic ... conclu- sions. 2 Automatic Semantic Role Labeling One of the goals of the FN project is to design a linguistic ontology that can be used for the auto- matic processing of semantic information. The as- sociated...

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... Juraf- sky. 2004. Semantic role labeling by tagging syntactic chunks. In Proceed- ings of CoNLL-2004, Shared Task – Semantic Role Labeling. Kadri Hacioglu. 2004a. A lightweight semantic chunking ... Linguistics, Stanford University, Stanford, CA 94305 jurafsky@stanford.edu Abstract Semantic role labeling is the process of annotating the predicate-argument struc- ture in text with semantic labels. ... predicate-argument structures for information extraction. In Proceedings of ACL, Sapporo, Japan. Nianwen Xue and Martha Palmer. 2004. Calibrating features for semantic role labeling. In Proceedings...

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... Improves Semantic Role Labeling Kristina Toutanova Dept of Computer Science Stanford University Stanford, CA, 94305 kristina@cs.stanford.edu Aria Haghighi Dept of Computer Science Stanford University Stanford, ... we are able to use information about other argument nodes when labeling a node. Final Pipeline Here we describe the application in testing of a joint model for semantic role labeling, using a local model ... predicate-argument structures for in- formation extraction. In Proceedings of ACL-2003. Cynthia A. Thompson, Roger Levy, and Christopher D. Man- ning. 2003. A generative model for semantic role labeling. In Proceedings...

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... method for reducing the annotation effort involved in creating resources for semantic role labeling. Our strategy is to ex- pand a manually annotated corpus by projecting semantic role information ... sen- tence we wish to annotate. Semantic role labeler We evaluated our method on a semantic role labeling task. Specifically, we compared the performance of a generic seman- tic role labeler trained on ... semantic role labeling liter- ature (see Baker et al. (2007) for an overview). SVM classifiers were trained to identify the argu- ments and label them with appropriate roles. For the latter we performed...

Ngày tải lên: 08/03/2014, 21:20

... strategies for semantic role la- beling. Journal of Artificial Intelligence Research, 29:105–151. K. Toutanova, A. Haghighi, and C.D. Manning. 2008. A global joint model for semantic role labeling. Computational ... tasks. 1 Introduction Semantic Role Labeling (SRL) is the process of assign- ing semantic roles to strings of words in a sentence ac- cording to their relationship to the semantic predicates expressed ... parser errors account for many of the errors of our system. This analysis also suggests that simultaneous incremental parsing and semantic role labeling may lead to performance gains in both...

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... without substitution. In this pa- per, we call a tuple of semantic frame and semantic role a semantic signature. Two phrase pairs with the same semantic signature are considered valid substi- tutions ... can be unaligned only if they are inside the semantic role constituent. Utilizing these rules, we can perform the sentence generation process. For each semantic structure of each sentence, 2 we ... improvement on Chinese- English machine translation tasks and the performance is comparable to system trained with larger manually collected parallel corpus. While our experiments were performed on Chinese- English,...

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... and 0.47 for the complete task. 1 Introduction Semantic role labeling (SRL), the process of auto- matically identifying arguments of a predicate in a sentence and assigning them semantic roles, ... Sweden {richard, pierre}@cs.lth.se Abstract We present a FrameNet-based semantic role labeling system for Swedish text. As training data for the system, we used an annotated corpus that we produced by transferring ... instances for the start classifier, 500,000 for the end classifier, and 272,000 for the role classifier. The features used by the classifiers are described in Subsec- tion 3.2, and the performance...

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... one predicate. The possible semantic roles are pre-defined for each predicate. The set of roles depends on the corpora. SRL is becoming an important tool for infor- mation extraction, text ... each predicate. It is difficult to define universal semantic roles for all predicates. That is why PropBank defines a set of semantic roles for each possible sense of each predicate (frame) ... hand-annotated corpus. It transforms sentences into propositions. It adds a semantic layer to the Penn TreeBank (Marcus et al, 1994) and defines a set of semantic roles for each predicate. It is...

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