A Novel Use of Statistical Parsing to Extract Information from Text

By Scott Miller, Heidi Fox, Lance A. Ramshaw, Ralph M. Weischedel. 2000

237 citations recorded by the source corpus.

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In Information Extraction

Citations in the corpus

Natural Language Processing (Almost) from Scratch (2011, background): “Musillo and Merlo (2006) also describe a negative result at the same joint task.”
Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation (2003, methodology): “Generated Headline: Kurdish guerilla forces poured into Kirkuk after Iraqi troops abandoned oil fields.”
Head-Driven Statistical Models for Natural Language Parsing (2003, background): “Miller et al. (2000) develop generative, lexicalized models for information extraction of relations.”
Dependency Tree Kernels for Relation Extraction (2004, methodology): “Miller et al. (2000) propose an integrated statistical parsing technique that augments parse trees with semantic labels denoting entity and relation types.”
Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction (2004, methodology): “For the Template Relations task of MUC-7, BBN researchers (Miller et al., 2000) augmented syntactic parse trees with semantic information corresponding to entities and relations and built generative models for the augmented trees.”
Structured Models for Fine-to-Coarse Sentiment Analysis (2007, methodology): “This includes parsing and relation extraction (Miller et al., 2000), entity labeling and relation extraction (Roth and Yih, 2004), and part-of-speech tagging and chunking (Sutton et al., 2004).”
Joint Parsing and Semantic Role Labeling (2005, background): “For example, Miller et al. (2000) showed that performing parsing and information extraction in a joint model improves performance on both tasks.”
A Statistical Semantic Parser that Integrates Syntax and Semantics (2005, methodology): “The syntactic model in (Miller et al., 2000) is similar to Collins’, but does not use features like subcat frames and distance measures.”
Semi-supervised Relation Extraction with Large-scale Word Clustering (2011, methodology): “Then the feature based method explicitly extracts a variety of lexical, syntactic and semantic features for statistical learning, either generative or discriminative (Miller et al., 2000; Kambhatla, 2004; Boschee et al., 2005; Grishman et al., 2005; Zhou et al., 2005; Jiang and Zhai, 2007).”
Composition of Conditional Random Fields for Transfer Learning (2005, unknown): “Finally, Miller et al. (Miller et al., 2000) have combined entity recognition, parsing, and relation extraction into a jointly-trained single statistical parsing model that achieves improved performance on all the subtasks.”
A unified architecture for natural language processing: deep neural networks with multitask learning (2008, methodology): “Similarly, in (Miller et al., 2000) NER, parsing and relation extraction were jointly trained in a statistical parsing model achieving improved performance on all tasks.”
Automatic Labeling of Semantic Roles (2000, methodology): “For example, in the context of the Air Traveler Information System (ATIS) for spoken dialogue, Miller et al. (1996) computed the probability that a constituent such as Atlanta filled a semantic slot such as DESTINATION in a semantic frame for air travel.”
Relation Extraction Using Label Propagation Based Semi-Supervised Learning (2006, methodology): “Many machine learning methods have been proposed to address this problem, e.g., supervised learning algorithms (Miller et al., 2000; Zelenko et al., 2002; Culotta and Soresen, 2004; Kambhatla, 2004; Zhou et al., 2005), semi-supervised learning algorithms (Brin, 1998; Agichtein and Gravano, 2000;…”
Extracting Relations with Integrated Information Using Kernel Methods (2005, methodology): “Prior approaches to this task (Miller et al., 2000; Zelenko et al., 2003) have relied on partial or full syntactic analysis.”
Exploring Various Knowledge in Relation Extraction (2005, methodology): “Miller et al (2000) augmented syntactic full parse trees with semantic information corresponding to entities and relations, and built generative models for the augmented trees.”