Efficient Inference and Structured Learning for Semantic Role Labeling
نویسندگان
چکیده
منابع مشابه
Efficient Inference and Structured Learning for Semantic Role Labeling
We present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this area, which has resorted to either approximate methods or off-theshelf integer linear programming solvers. In addition, it allows training a globally-normalized log-linear model with res...
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We show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach, we use monolingual SRL systems to produce argument candidates for predicates in bitext at first. Then, we simultaneously generate SRL results for two sides of bitext using our joint inference model. Our model prefers the bilingual SRL result that is not only reasonable ...
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This paper presents our work on Semantic Role Labeling using a Transformation-Based ErrorDriven approach in the style of Eric Brill (Brill, 1995). Our approach achieved an overall F1 score of 43.48 on non-verb annotations. We believe our approach is noteworthy because of its novelty in this area and because it produces short lists of human-understandable transformation rules as its output.
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In Semantic Role Labeling (SRL), it is reasonable to globally assign semantic roles due to strong dependencies among arguments. Some relations between arguments significantly characterize the structural information of argument structure. In this paper, we concentrate on thematic hierarchy that is a rank relation restricting syntactic realization of arguments. A loglinear model is proposed to ac...
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ژورنال
عنوان ژورنال: Transactions of the Association for Computational Linguistics
سال: 2015
ISSN: 2307-387X
DOI: 10.1162/tacl_a_00120