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Sentence Classification and Clause Detection for Croatian . Kristina Vučković, Željko Agić, Marko Tadić Department of Information Sciences, Department of Linguistics Faculty of Humanities and Social Sceinces, University of Zagreb {kvuckovi, zagic, [email protected] FASSBL 7 Conference

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Sentence Classification and Clause Detection for Croatian

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Sentence Classification and Clause Detection for Croatian

Kristina Vučković, Željko Agić, Marko Tadić

Department of Information Sciences, Department of LinguisticsFaculty of Humanities and Social Sceinces, University of Zagreb

{kvuckovi, zagic, [email protected]

FASSBL 7 Conference

Dubrovnik, Croatia2010-10-05


Overview

  • What?

    • classifying Croatian sentences by structure

    • detecting independent and dependent clauses

  • How?

    • implemented a prototype system in NooJ

    • linked it with a morphosyntactic tagger

    • evaluated on a sample from Croatian corpora

  • Why?

    • rule-based chunking and shallow parsing


Classification and detection

  • sentence segmentation is easy when considering sentence boundaries only

  • here, we:

    • detect boundaries of clauses in complex sentences

    • assign type to sentences

  • sentence classification

    • purpose: declarative, interrogative, etc.

    • structure: simple and complex

  • complex sentences

    • independent complex, i.e. compound sentences

    • dependent complex sentences


Classification and detection

  • independent complex sentences

    • independent clause connected to the main clause by using a conjunction

    • type defined by the choice of conjunction

      • e.g. constituent clause, conjunctions {i, pa, te, ni, niti}

      • disjunctive, opposite, exclusive, conclusive and explanatory clause

      • Svi su spavali, jedino sam ja bio budan. (exclusive)

  • dependent complex sentences

    • main clause is independent, all the others depend on it and cannot stand alone in a sentence

      • Predicative, subjective, objective, attributive, appositional and adverbial clause

      • Ispričat ću tišto mi se dogodilo.(objective)


The system

  • prototype implemented in NooJ

    • finite state transducer cascades (local grammars)

    • Croatian lexical resources

    • each cascade detects and annotates a different type of clause

    • built on top of a chunker for Croatian

  • the top-level grammar

    • two types of subgraphs: main clauses and independent clauses


The system

  • Main clause grammar

    • presence of a VP and possibly any other phrase

    • independent clauses recognized just by using the conjunctions

    • implementation of dependent clause detection varies across clause types


Experiment setup

  • used the CW100 corpus

    • XCES-encoded to word level

    • sentence delimited, tokenized, manually lemmatized and MSD-annotated

    • 200 randomly selected sentences

      • 100 for the development and 100 for testing

  • utilized the CroTag tagger

    • NooJ input format allows external annotation

    • created three systems

      • no preprocessing

      • tagging input sentences with CroTag (~85% accuracy)

      • using the manually assigned tags from CW100

  • recall, precision, F1-measure


Results

  • scores for the three systems

    • “perfect” tagging system is the top-performer

    • benefits of automatic tagging?

  • distribution of assigned types

    • main, objective, opposite, adverbial, attribute, ...

  • misclassifications

    • attributive and objective most commonly misclassified

    • data sparseness


Conclusions and future work

  • the system scores good in terms of F1-measure

    • open issues

      • verb coordination

      • dislocated nominal predicates

      • attribute classes starting with a PP

      • complex insertion of dependent clauses

    • no real benefit from automatic MSD-tagging

  • future work

    • resolving the issues

    • re-evaluation on a larger test set?

    • integration with a rule-based shallow parser


Thank you for your attention.

The research within the project ACCURAT leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), grant agreement no 248347.

www.accurat-project.eu


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