PhD thesis - School of Informatics - University of Edinburgh

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Chapter 5. Parsing English Inclusions 145 The multi-word inclusion markup helps both parsers partially with the identification of proper noun phrases, and partially with the detection of other multi-word ex- pressions. As a result, it would be interesting to derive a way of interfacing the parser either with a named entity recogniser or a multi-word expression extractor, in addi- tion to the English inclusion classifier, in order to determine the difference in parsing performance on the stratified random and inclusion sets. 5.5 Chapter Summary This chapter started out with the hypothesis that English inclusions can constitute a significant source of error for monolingual parsers. Evidence for this hypothesis was provided both for a treebank-induced parser and a rule-based grammar parser for Ger- man. It was shown that the English inclusion classifier is able to detect sentences that are difficult to parse, or which the parser is unable to parse. When interfacing English inclusion detection with parsing, ensuring that inclusions are treated as phrases, parsing performance increases significantly for the treebank-induced parser. When con- ducting a similar experiment with the rule-based grammar parser, coverage increases substantially and ambiguity decreases. This shows that foreign inclusions present a non-negligible problem to monolingual parsers. Their correct detection avoids the cas- cading of errors in the remainder of the parse tree. As parsers are integrated into many NLP applications, including text-to-speech synthesis or machine translation, improved parsing of foreign material in mixed-lingual text will also have positive effects on the performance of any subsequent language processing. The direct benefit of English inclusion detection to such applications is investigated in the next chapter.
Chapter 6 Other Potential Applications This chapter discusses in detail three other applications or fields, namely text-to- speech synthesis, machine translation as well as linguistics and lexicography, for which the automatic identification of foreign, in particular English, inclusions would be beneficial. In Section 6.1, the benefit and implications of applying English inclusion detection as a pre-processing step in a text-to-speech (TTS) synthesiser are discussed in greater detail and a strategy for an extrinsic evaluation of the inclusion classifier is proposed. This section includes detailed reviews of lessons learnt from production and perception experiments with mixed-lingual speech and conclusions from studies in the field of second language acquisition (SLA). Reviews of research efforts on all aspects of synthesising speech containing foreign inclusions are also presented in detail. Sections 6.2 and 6.3, which summarise the value of English inclusion detection for machine translation as well as linguistics and lexicography, are less detailed but present several ideas to pursue in future work. 146
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Automatic Detection of English Incl
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these parsers with the annotation-f
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Declaration I declare that this the
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3.3.5 Post-processing Module . . .
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A.2.2 Kappa Coefficient . . . . . .
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5.6 Average relative token frequenc
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3.16 Most frequent English inclusio
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Chapter 1. Introduction 2 siderable
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Appendix C. TIGER Tags and Labels 1
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Bibliography 198 Andersen, G. (2005
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Bibliography 200 Bresnan, J. (2001)
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Bibliography 202 Damashek, M. (1995
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Bibliography 204 Finkel, J., Dingar
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Bibliography 206 Hachey, B., Alex,
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Bibliography 208 Kirkness, A. (1984
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Bibliography 210 and Technology (In
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Bibliography 212 Poplack, S. (1988)
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Bibliography 214 Sokol, D. K. (2000
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Bibliography 216 Yang, W. (1990). A
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PhD thesis - School of Informatics - University of Edinburgh

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