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Stanford typed dependencies manual
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The Stanford typed dependencies representation was designed to provide a simple description of the grammatical relationships in a sentence that can easily be understood and effectively used by people without linguistic expertise who want to extract textual relations.Abstract:
The Stanford typed dependencies representation was designed to provide a simple description of the grammatical relationships in a sentence that can easily be understood and effectively used by people without linguistic expertise who want to extract textual relations. In particular, rather than the phrase structure representations that have long dominated in the computational linguistic community, it represents all sentence relationships uniformly as typed dependency relations. That is, as triples of a relation between pairs of words, such as “the subject of distributes is Bell.” Our experience is that this simple, uniform representation is quite accessible to non-linguists thinking about tasks involving information extraction from text and is quite effective in relation extraction applications. Here is an example sentence:read more
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References
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Proceedings Article
Generating Typed Dependency Parses from Phrase Structure Parses
TL;DR: A system for extracting typed dependency parses of English sentences from phrase structure parses that captures inherent relations occurring in corpus texts that can be critical in real-world applications is described.
Proceedings Article
Open information extraction from the web
TL;DR: Open Information Extraction (OIE) as mentioned in this paper is a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input.
Proceedings ArticleDOI
The Stanford Typed Dependencies Representation
TL;DR: This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding, and considers the underlying design principles of the Stanford scheme.
Proceedings ArticleDOI
Movie review mining and summarization
Li Zhuang,Feng Jing,Xiaoyan Zhu +2 more
TL;DR: A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge, and the experimental results show the effectiveness of the proposed approach in movie review mining and summarization.
Proceedings Article
Open Information Extraction Using Wikipedia
Fei Wu,Daniel S. Weld +1 more
TL;DR: WOE is presented, an open IE system which improves dramatically on TextRunner's precision and recall and is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data.