The necessity of parsing for predicate argument recognition
Published 1 January 2001Open access
Daniel Gildea, Martha Palmer
Citations218
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The effect of parser accuracy on these systems' performance is quantified, and the question of whether a flatter "chunked" representation of the input can be as effective for the purposes of semantic role identification is examined.
Abstract
Broad-coverage corpora annotated with semantic role, or argument structure, information are becoming available for the first time. Statistical systems have been trained to automatically label semantic roles from the output of statistical parsers on unannotated text. In this paper, we quantify the effect of parser accuracy on these systems' performance, and examine the question of whether a flatter "chunked" representation of the input can be as effective for the purposes of semantic role identification.
Keywords
Computer ScienceBiochemistry, Genetics and Molecular Biology
ScholarlyCommons (University of Pennsylvania)Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
12,978 Citations2001John Lafferty, Andrew McCallum +1 more
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
The Berkeley FrameNet Project
2,564 Citations1998Collin F. Baker, Charles J. Fillmore +1 more
This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.
Computational LinguisticsAutomatic Labeling of Semantic Roles
1,647 Citations2002Daniel Gildea, Daniel Jurafsky
Maximum Entropy Markov Models for Information Extraction and Segmentation
1,333 Citations2000Andrew McCallum, Dayne Freitag +1 more
A new Markovian sequence model is presented that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences.
Three generative, lexicalised models for statistical parsing
749 Citations1997Michael Collins
A new statistical parsing model is proposed, which is a generative model of lexicalised context-free grammar and extended to include a probabilistic treatment of both subcategorisation and wh-movement.
Language Resources and EvaluationFrom treebank to propbank
538 Citations2002Paul Kingsbury, Martha Palmer
This paper describes the approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank, and introduces metaframes as a technique for handling similar frames among near− synonymous verbs.
Statistical parsing with a context-free grammar and word statistics
535 Citations1997Eugene Charniak
A parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence that outperforms previous schemes is described.
Language<b>The Core Language Engine</b> . Ed. by Hiyan Alshawi. (A Bradford book.) Cambridge, MA: The MIT Press, 1992. Pp. xviii, 322. $37.50.
350 Citations1994Michael A. Covington
Introduction to the CLE, Hiyan Alshawi and Robert C. Moore - language analysis and interpretation, overview of CLE components logical forms, and levels of logical form in the CLE.
arXiv (Cornell University)FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text
255 Citations1997Jerry R. Hobbs, Douglas E. Appelt +5 more
This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures.
Representing sentence structure in hidden Markov models for information extraction
123 Citations2001Soumya Ray, Mark Craven
An approach to representing the grammatical structure of sentences in the states of the model by using an objective function during HMM training which maximizes the ability of the learned models to identify the phrases of interest is proposed.
Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)Algorithms That Learn to Extract Information BBN: Description of the Sift System as Used for MUC-7
72 Citations1998S.L. Miller, Michael Crystal +5 more
For MUC-7, BBN has for the first time fielded a fully-trained system for NE, TE, and TR; results are all the output of statistical language models trained on annotated data, rather than programs executing handwritten rules.