Applying Repair Processing in Chinese Homophone Disambiguation

Applying Repair Processing in Chinese Homophone Disambiguation Yue-Shi Lee and Hsin-Hsi Chen Department of Computer Science and Information Engineering National Taiwan University Taipei, Taiwan, R.O.C. E-Mail: { leeys, hh_chen } @csie.ntu.edu.tw Abstract Repair processing plays an important role in spoken language processing systems. This paper proposes a method for correcting Chinese repetition repairs and demonstrates the effects of repair processing in Chinese homophone disambiguation. The experimental results show that the precision rate of 93.87% and the recall rate of 90•65% can be achieved for the repair processing. At the same time, 50% of errors in the repairing segments can be reduced for Chinese homophone disambiguation. 1 Introduction Repair is a very common phenomenon in spoken languages. Speakers usually repeat, add, replace, or even abandon some constructions in the utterances for some mental reasons. Typical repairs in Chinese spoken data' are shown in (1) and (2). (i) • ..~2 ~ ~ t~ ~%-- I drink wine too SHI . . sometimes SHI ...(1.3)~__ ^;~-- not too ..~F~ ~$fJ ~.\ not too constrain-oneself particle (I am sometimes addicted to drinking.) The transcription system proposed by Bois, et al. (1992) is used to transcribe the spoken data. The three symbols ...(N) .... and .. denote an unfilled pause (silence) is long, medium and short, respectively. The symbol % denotes the glottal stop. (2) because eye • .1~1~ -- N.\ eye once close (Because eye .. Once (you) close your eyes .... ) Heeman and Allen (1994) describe that 25% of turns contain at least one speech repair in their corpus. In our study, 17% of turns contain at least one speech repair• Thus, the repair processing cannot be negligible and has influences to a certain extent. Recently, text-first approach and speech-first approach have been proposed to touch on repairs in English. The text-first approach assumes the speech recognizer could provide a correct transcription. That is, it tries to detect and correct speech repairs automatically using text alone• Hindle (1983) adds rules to a deterministic parser to tackle the problem of correcting speech repairs. His parsing strategy depends on the successful disambiguation of the syntactic categories. Although syntactic categories can be determined well by local context, Hindle admits that speech repair disrupts the local context. Bear, et al. (1992) firstly try to parse the input sentence and then invoke a repair processing when the parsing fails• For repair processing, a simple pattern matcher finds the candidates based on the lexical cues at the first stage. Then the syntactic and semantic processing filters out the impossible candidates. Heeman and Allen (1994) present an algorithm that detects and corrects modification and abridged repairs. The algorithm uses some repair patterns to capture potential repairs. These patterns are built based on the identification of word fragments, editing terms 2, and word correspondences between the repaired segment 2 The editing terms can either be filled pauses (e.g., urn, un, er) or cue phrases (e.g., I mean, 1 guess, well). 57 and the repairing segment 3. The resulting potential repairs …

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