Automatic Sentence Structure Annotation for Spoken Language Processing

155 Pages·2012·1.22 MB·English

Automatic Sentence Structure Annotation
for Spoken Language Processing
DustinLundringHillard
Adissertation submitted inpartialful(cid:2)llmentof
therequirements forthedegreeof
DoctorofPhilosophy
UniversityofWashington
2008
ProgramAuthorized toOfferDegree: ElectricalEngineering

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ChairoftheSupervisory Committee:
MariOstendorf
ReadingCommittee:
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AndreasStolcke
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Abstract
AutomaticSentenceStructureAnnotation
forSpokenLanguageProcessing
DustinLundringHillard
ChairoftheSupervisory Committee:
ProfessorMariOstendorf
ElectricalEngineering
Increasing amounts of easily available electronic data are precipitating a need for automatic pro-
cessing that can aid humans in digesting large amounts of data. Speech and video are becoming
an increasingly signi(cid:2)cant portion of on-line information, from news and television broadcasts, to
oralhistories, on-line lectures, oruser generated content. Automatic processing ofaudioand video
sources requires automatic speech recognition (ASR) in order to provide transcripts. Typical ASR
generates only words, without punctuation, capitalization, or further structure. Many techniques
availablefromnaturallanguageprocessingthereforesufferwhenappliedtospeechrecognitionout-
put,becausetheyassumethepresenceofreliablepunctuationandstructure. Inaddition,errorsfrom
automatic transcription also degrade the performance of downstream processing such as machine
translation, name detection, or information retrieval. We develop approaches for automatically
annotating structure in speech, including sentence and sub-sentence segmentation, and then turn
towardsoptimizing ASRandannotation fordownstreamapplications.
The impact of annotation is explored at the sentence and sub-sentence level. We describe our
generalapproachforpredictingsentencesegmentationanddealingwithuncertainty inASR.Anew
ASRsystem combination approach isdescribed that improves ASRmore than any previously pro-
posedmethods. Theimpactofautomaticsegmentationinmachinetranslation isalsoevaluated,and
we(cid:2)ndthat optimizing segmentation directly fortranslation improves translation quality, perform-
ing as well (or better than) using reference segmentation. Turning to sub-sentence annotation, we

describe approaches for supervised comma detection and unsupervised learning of prosodic struc-
ture. Theutility ofautomatic commasisthen assessed inthecontext ofinformation extraction and
machine translation. Including commas in information extraction tasks signi(cid:2)cantly improves per-
formance, especially when punctuation precision and recall are optimized directly for entity and
relation extraction. We then also propose approaches for improving translation reordering models
withcuesfromcommasandsentence structure.