Acl4 project nclt seminar presentation 7th june 2006 conor cafferkey
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ACL4 Project NCLT Seminar Presentation, 7th June 2006 Conor Cafferkey. Towards Parsing Unrestricted Text into PropBank Predicate-Argument Structures. Project Overview. Open research problem: Integrating syntactic parsing and semantic role labeling (SRL) Approach

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Towards Parsing Unrestricted Text into PropBank Predicate-Argument Structures

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Acl4 project nclt seminar presentation 7th june 2006 conor cafferkey

ACL4 Project

NCLT Seminar Presentation, 7th June 2006

Conor Cafferkey

Towards Parsing Unrestricted Text into PropBank Predicate-Argument Structures


Project overview

Project Overview

Open research problem:

  • Integrating syntactic parsing and semantic role labeling (SRL)

    Approach

  • Retraining a history-based generative lexicalized parser (Bikel, 2002)

  • Semantically-enriched training corpus (Penn Treebank + PropBank-derived semantic role annotations)


Treebank syntactic bracketing style

Treebank Syntactic Bracketing Style


Treebank syntactic bracketing style1

Treebank Syntactic Bracketing Style


Semantic roles

Semantic Roles

  • Relationship that a syntactic constituent has with a predicate

  • Predicate-argument relations

  • PropBank (Palmer et al., 2005)


Propbank predicate argument relations

PropBank Predicate-Argument Relations

Frameset: hate.01

ARG0: experiencer

ARG1: target


Propbank argument types

PropBank Argument Types

  • ARG0 - ARG5: arguments associated with a verb predicate, defined in the PropBank Frames scheme.

  • ARGM-XXX: adjunct-like arguments of various sorts, where XXX is the type of the adjunct. Types include locative (LOC), temporal (TMP) , manner (MNR), etc.

  • ARGA: causative agents.

  • rel: the verb of the proposition.


Current approaches

Current Approaches

  • Semantic role labeling (SRL) task:

    • Identify, given a verb:

      • which nodes of the syntactic tree are arguments of that verb, and

      • what semantic role each such argument plays with regard to the verb.


Current approaches1

Current Approaches

  • “Pipelined” approach

  • Parsing → Pruning → ML-techniques → post-processing

  • CoNLL-2005 (Carreras and Márquez, 2005)

    • SVM, Random Fields, Random Forests, …

    • Various lexical parameters


An integrated approach to semantic parsing

An Integrated Approach to Semantic Parsing

  • Integrate syntactic and semantic parsing

  • Retrain parser using semantically-enriched corpus (Treebank + PropBank-derived semantic roles)

  • Parser itself performs semantic role labeling (SRL)


Project components

Project Components

  • “Off-the-shelf”:

    • Parser (Bikel, 2002) emulating Collins’ (1999) model 2

    • Penn Treebank Release 2 (Marcus et al., 1993)

    • PropBank 1.0 (Palmer, 2005)

  • Written for project (mainly in Python):

    • Scripts to annotate Treebank with PropBank data

    • Script to generate new head-finding rules for Bikel’s parser

    • SRL evaluation scripts

    • Utility scripts (pre-processing, etc.)


Appending semantic roles to treebank syntactic category labels

Appending Semantic Roles to Treebank Syntactic Category Labels

wsj/15/wsj_1568.mrg 16 2 gold hate.01 vn--a 0:1-ARG0 2:0-rel 3:1-ARG1


Syntactic bracketing evaluation

Syntactic Bracketing Evaluation

  • Parseval measures (Black, et al., 1992)


Syntactic bracketing evaluation1

Syntactic Bracketing Evaluation

  • Harmonic mean of precision and recall:


Baseline syntactic bracketing performance

Baseline Syntactic Bracketing Performance

Parse Time: 114:41

Parsing Section 00, trained with sections 02-21 of Penn Treebank (1918 sentences)


Semantically augmented treebanks

Semantically-Augmented Treebanks

  • N: augment node labels with ARGNs only

  • N-C: augment node label with conflated ARGNs only

  • M: augment node labels with ARGMs only

  • M-C: augment node labels with conflated ARGMs only

  • NMR: augment node labels with ARGNs, ARGMs and rels


Syntactic bracketing evaluation2

Syntactic Bracketing Evaluation

Parsing Section 00, trained with sections 02-21 of Penn Treebank (1918 sentences)


Semantic evaluation

Semantic Evaluation


Semantic evaluation1

Semantic Evaluation

  • Evaluating by terminal number and height

  • Evaluating by terminal span

  • How strictly to evaluate?


Semantic role labeling evaluation

Semantic Role Labeling Evaluation

Parsing Section 00, trained with sections 02-21 of Penn Treebank (1918 sentences)


Semantic role labeling evaluation1

Semantic Role Labeling Evaluation

Parsing Section 00, trained with sections 02-21 of Penn Treebank (1918 sentences)


Syntactic nodes that play multiple semantic roles

Syntactic Nodes that Play Multiple Semantic Roles


Adding more information

Adding More Information

  • Co-index the semantic role labels with governing predicate (verb)

  • i.e. include the appropriate roleset name in each semantic label augmentation


Co indexing the semantic augmentations

Co-indexing the Semantic Augmentations


Adding more information1

Adding More Information

  • Data sparseness

  • Time efficiency

  • Need to make some sort of generalizations

  • “Syntacto-semantic” verb classes

  • VerbNet (Kipper et al., 2002)


Co indexing with verbnet classes

Co-indexing with VerbNet classes


Future ideas

Future Ideas

  • Integrate the (un co-indexed) output from the re-trained parser into a pipelined SRL system

  • Syntactic parsing informed by semantic roles?

    • Recoding the parser to take better advantage of the semantic roles

    • Reranking n-best parser outputs based on semantic roles


Summary

Summary

  • Retrained a history-based generative lexicalized parser with semantically-enriched corpus

    • Corpus annotation

    • Generating head-finding rules

  • Evaluated parser’s performance

    • Syntactic parsing (evalb)

    • Semantic parsing (SRL)


References

References

  • Bikel, Daniel M. 2002. Design of a Multi-lingual, Parallel-processing Statistical Parsing Engine. In Proceedings of HLT2002, San Diego, California.

  • Black, Ezra, Frederick Jelinek, John D. Lafferty, David M. Magerman, Robert L. Mercer and Salim Roukos. 1992. Towards History-based Grammars: Using Richer Models for Probabilistic Parsing. In Proceedings DARPA Speech and Natural Language Workshop, Harriman, New York, pages 134-139. Morgan Kaufmann.

  • Carreras, Xavier and Lluís Màrquez. 2005. Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling. In Proceedings of CoNLL-2005, pages152-164.

  • Collins, Michael John. 1999. Head-driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania, Philadelphia.


References1

References

  • Kipper, Karin, Hoa Trang Dang and Martha Palmer. 2000. Class-Based Construction of a Verb Lexicon. In Proceedings of Seventeenth National Conference on Artificial Intelligence, Austin, Texas.

  • Marcus, Mitchell P., Beatrice Santroini and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313-330.

  • Palmer, Martha, Daniel Gildea and Paul Kingsbury. 2005. The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics, 31(1):71-106.

  • Yi, Szu-ting and Martha Palmer. 2005. The integration of syntactic parsing and semantic role labeling. In Proceedings of CoNLL-2005, pages 237-240.


Towards parsing unrestricted text into propbank predicate argument structures

http://student.dcu.ie/~cafferc2/


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