Language-independent Approach to High Quality Dependency Selection from Automatic Parses
نویسندگان
چکیده
منابع مشابه
High Quality Dependency Selection from Automatic Parses
Many NLP tasks such as question answering and knowledge acquisition are tightly dependent on dependency parsing. Dependency parsing accuracy is always decisive for the performance of subsequent tasks. Therefore, reducing dependency parsing errors or selecting high quality dependencies is a primary issue. In this paper, we present a supervised approach for automatically selecting high quality de...
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While the average performance of statistical parsers gradually improves, they still attach to many sentences annotations of rather low quality. The number of such sentences grows when the training and test data are taken from different domains, which is the case for major web applications such as information retrieval and question answering. In this paper we present a Sample Ensemble Parse Asse...
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Under multi-headed dependency grammar, a parse is a connected DAG rather than a tree. Such formalisms can be more syntactically and semantically expressive. However, it is hard to train, test, or improve multi-headed parsers because few multi-headed corpora exist, particularly for the projective or planar case. To help fill this gap, we observe that link grammar already produces undirected plan...
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The average results obtained by unsupervised statistical parsers have greatly improved in the last few years, but on many specific sentences they are of rather low quality. The output of such parsers is becoming valuable for various applications, and it is radically less expensive to create than manually annotated training data. Hence, automatic selection of high quality parses created by unsup...
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ژورنال
عنوان ژورنال: Journal of Natural Language Processing
سال: 2014
ISSN: 1340-7619,2185-8314
DOI: 10.5715/jnlp.21.1163