Efficient Dependency-Guided Named Entity Recognition

Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the other hand, also convey crucial semantic-level information. It has been shown previously that such information can be used to improve the performance of NER (Sasano and Kurohashi 2008, Ling and Weld 2012). In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER. Specifically, unlike existing approaches which only exploit dependency information for designing local features, we show that certain global structured information of the dependency trees can be exploited when building NER models where such information can provide guided learning and inference. Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time.

[1]  Danqi Chen,et al.  A Fast and Accurate Dependency Parser using Neural Networks , 2014, EMNLP.

[2]  Tom M. Mitchell,et al.  Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary , 2015, TACL.

[3]  Philipp Koehn,et al.  Statistical Significance Tests for Machine Translation Evaluation , 2004, EMNLP.

[4]  Wei Lu,et al.  Learning to Recognize Discontiguous Entities , 2016, EMNLP.

[5]  Joakim Nivre,et al.  MaltParser: A Data-Driven Parser-Generator for Dependency Parsing , 2006, LREC.

[6]  M. Aigner,et al.  Proofs from "The Book" , 2001 .

[7]  William W. Cohen,et al.  Semi-Markov Conditional Random Fields for Information Extraction , 2004, NIPS.

[8]  Jun'ichi Tsujii,et al.  Improving the Scalability of Semi-Markov Conditional Random Fields for Named Entity Recognition , 2006, ACL.

[9]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[10]  Sadao Kurohashi,et al.  Japanese Named Entity Recognition Using Structural Natural Language Processing , 2008, IJCNLP.

[11]  Dan Roth,et al.  Joint Mention Extraction and Classification with Mention Hypergraphs , 2015, EMNLP.

[12]  Daniel S. Weld,et al.  Fine-Grained Entity Recognition , 2012, AAAI.

[13]  Ni Lao,et al.  Relational retrieval using a combination of path-constrained random walks , 2010, Machine Learning.

[14]  Christopher D. Manning,et al.  Joint Parsing and Named Entity Recognition , 2009, NAACL.

[15]  Hoifung Poon,et al.  Unsupervised Semantic Parsing , 2009, EMNLP.

[16]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[17]  Martin Aigner,et al.  Proofs from THE BOOK , 1998 .

[18]  Christopher D. Manning,et al.  Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data , 2010, ACL.

[19]  Andrew McCallum,et al.  Maximum Entropy Markov Models for Information Extraction and Segmentation , 2000, ICML.

[20]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[21]  Dan Klein,et al.  Learning Dependency-Based Compositional Semantics , 2011, CL.

[22]  Dan Roth,et al.  Design Challenges and Misconceptions in Named Entity Recognition , 2009, CoNLL.

[23]  Wei Lu,et al.  Weak Semi-Markov CRFs for Noun Phrase Chunking in Informal Text , 2016, HLT-NAACL.

[24]  Wei Li,et al.  Early results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons , 2003, CoNLL.

[25]  Malvina Nissim,et al.  Exploring the boundaries: gene and protein identification in biomedical text , 2005, BMC Bioinformatics.

[26]  Phil Blunsom,et al.  Semantic Role Labelling with Tree Conditional Random Fields , 2005, CoNLL.

[27]  Heeyoung Lee,et al.  Joint Entity and Event Coreference Resolution across Documents , 2012, EMNLP.

[28]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[29]  Jian Su,et al.  Named Entity Recognition using an HMM-based Chunk Tagger , 2002, ACL.

[30]  Paola Velardi,et al.  Unsupervised Named Entity Recognition Using Syntactic and Semantic Contextual Evidence , 2001, CL.

[31]  Minlie Huang,et al.  Recognizing Biomedical Named Entities Using Skip-Chain Conditional Random Fields , 2010, BioNLP@ACL.

[32]  Claire Cardie,et al.  Extracting Opinion Expressions with semi-Markov Conditional Random Fields , 2012, EMNLP.