The dissertation of Andreas Stolcke is approved: University of ...

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CHAPTER 4. STOCHASTIC CONTEXT-FREE GRAMMARS 102--> dog (70)ADJ --> big (75)--> old (72)VI --> ate (29)--> slept (28)VT --> heard (24)--> saw (19)This is essentially the phrase structure of the target grammar, except that the recursion in noun phrases isimplemented differently. 16On the other hand, when given the unordered samples, the algorithm produced a grammar thatcontained nine redundant rules. However, the same grammar as above was found by removing these rules,restarting the search and pruning three more redundant rules.With ordered samples, 16 of the 100 samples required creation of new rules, and 212 merging stepswere necessary to produce the final grammar, taking 49 seconds total CPU time. The unordered samples, onthe other hand, lead to new productions for 20 samples, 1374 merges, and took 174 seconds.For sample B, the outcome was similar. Ordering the samples by length resulted in the followinggrammar, an acceptable rendering of the original.S --> NP VP (100)VP --> V NP (295)NP --> DET N (395)--> NP RC (195)RC --> REL VP (195)DET --> a (196)--> the (199)N --> cat (137)--> dog (118)--> mouse (140)REL --> that (195)V --> heard (152)--> saw (143)A comparison experiment on the unordered list of samples had to be aborted due to excessively long run time.speed and accuracy.There are several reasons why ordering samples by length for incremental merging leads to improved‘Similar’ samples, which eventually lead to a single generalization are presented closer together, thereby¡reducing the time it takes to hypothesize and accept a generalization.As a result, the number of samples and corresponding states that are not yet fully merged into the model¡structure are minimized, reducing the search space overall.16 A beam search on the same data produced a grammar that was actually better than the target grammar, i.e., it was weakly equivalent,produced essentially the same phrase structure and used shorter rules.
CHAPTER 4. STOCHASTIC CONTEXT-FREE GRAMMARS 103Rules learned from smaller samples tend to be useful in structuring larger samples (but not the other¡way around). Thus the analyses of the previous samples can effectively guide the search for mergesbased on the longer, more recent ones.An interesting related result from non-probabilistic language induction is that strictly (lexicographically)ordered sample presentation makes the learning problem for certain classes of grammar provably easier(Porat & Feldman 1991).4.5.4 Summary and DiscussionThe above artificial examples show that SCFG learning based purely on distributional evidence andthe generic Bayesian simplicity bias can correctly identify several of the common linguistic structures. It alsoshows that this limited evidence can be misleading, especially when it comes to finding the ‘right’ phrasestructures. This is hardly surprising, given that a lot of the cues for human judgments of linguistic structurepresumable come from other sources, such as the semantic referents of the syntactic elements, phonologicalcues, morphological markers, etc. (Morgan et al. 1987).In a brief experiment, we applied our algorithm to a 1200 sentence corpus collected with the BeRPspeech system (Jurafsky et al. 1994a). The algorithm produced mostly plausible lexical categories and a largenumber of chunks corresponding to frequent phrases and collocations. However, the generalization achievedwas nowhere near what would be required for a sufficient coverage of new data. The problems can partlybe addressed by simple preprocessing steps, such as tagging of lexical items using standard probabilisticapproaches that achieve reasonable performance on data of this sort (Kupiec 1992b). Non-traditional phrasestructuring may not be a problem if sentence-level generalization is the main goal for an application. Also,bracketing may be induced separately using bracketing models trained from structured data (Brill 1993). Weplan to investigate such hybrid strategies in the future.
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The dissertation of Andreas Stolcke
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Bayesian Learning of Probabilistic
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iAcknowledgmentsLife and work in Be
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iiiContentsList of FiguresList of T
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CONTENTSv4.5.4 Summary and Discussi
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CHAPTER 1. INTRODUCTION 2Instance-b
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Consider the following problem: sta
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CHAPTER 8. FUTURE DIRECTIONS 1828.2
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184BibliographyAHO, ALFRED V., RAVI
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BIBLIOGRAPHY 186DAGAN, IDO, FERNAND
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BIBLIOGRAPHY 188——, & ——. 1
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BIBLIOGRAPHY 190QUINLAN, J. ROSS, &
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BIBLIOGRAPHY 192WALLACE, C. S., & P
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