Towards Robust Semantic Role Labeling
نویسندگان
چکیده
منابع مشابه
Towards Robust Semantic Role Labeling
Most research on semantic role labeling (SRL) has been focused on training and evaluating on the same corpus in order to develop the technology. This strategy, while appropriate for initiating research, can lead to over-training to the particular corpus. The work presented in this paper focuses on analyzing the robustness of an SRL system when trained on one genre of data and used to label a di...
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Hand-engineered feature sets are a well understood method for creating robust NLP models, but they require a lot of expertise and effort to create. In this work we describe how to automatically generate rich feature sets from simple units called featlets, requiring less engineering. Using information gain to guide the generation process, we train models which rival the state of the art on two s...
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Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use of the FrameNet database but fail to show suitable generalization capabilities in out-of-domain scenarios. In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distribution...
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ROBUST SEMANTIC ROLE LABELING USING PARSING VARIATIONS AND SEMANTIC CLASSES Szu-ting Yi Martha Stone Palmer Correctly identifying semantic entities and successfully disambiguating the relations between them and their predicates is an important and necessary step for successful natural language processing applications, such as text summarization, question answering, and machine translation. Rese...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2008
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli.2008.34.2.289