Extraction of Entities and Relations调研
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Extraction of Entities and Relations调研,后面将推出基于Stanford Parser 的具体实现
Phrase Dependency Parsing
• A lot of product features are phrases
• Phrase dependency parsing extends traditional dependency parsing tophrase level
• Relation extraction task can benefit from dependencies within a phrase.
The Approach
• (1) Constructing phrase dependency tree from results of chunking and dependency parsing;
• (2)Extracting candidate product features and candidate opinion expressions;
• (3) Extracting relations between product features and opinion expressions.
Phrase Dependency Tree
• Toolkit: Stanford parser,MXPOST
Candidate Product Features and Opinion Expressions
• Product Features
– All NPs and VPs are selected as candidate product features.
– Prepositional phrases (PPs) and adjectival phrases (ADJPs) are excluded.
• Opinion Expressions
– Use a dictionary which contains 8221opinion expressions to select candidates.
– The tree distance between product featureand opinion expression in a relation should be less than 5.
RelationExtraction
• Taking advantage of the kernel methods
• Instances containing similar relationswill share similar substructures in their dependency trees
• Generalize the definition by (Culotta andSorensen, 2004) to fit the phrase dependency tree.
Tree kernels fordependency trees
• List of features assigned to nodes
Tree kernels for dependency trees
• Matching function and similarity function
Kernel function for PDT
• Add term Kin to handle theinternal nodes of a phrase
Relation Extraction Experiment
• Corpus and features
• Restricting nodes by m(ti, tj) is a way toprune the search space of matching subtrees
Relation Extraction Experiment
ILP and SRL Approach
Extract Entity and Opinion on the Forum and Twitter
• Description of task:Who feels how on which aspects of which entity?
• Sentiment towards involved entities
• Sentiment towards participants
• Extracting sentiment expressions from Twitter
Reference
• Yejin Choi, Eric Breck, and Claire Cardie.2006. Joint extraction of entities and relations for opinion recognition. InProceedings of EMNLP.
• Aron Culotta and Jeffrey Sorensen. 2004.Dependency tree kernels for relation extraction. In Proceedings of ACL 2004.
• Yuanbin Wu, et al. 2009. Phrase Dependency Parsing for Opinion Mining.In Proceedings of EMNLP.