Syntactic natural language parsers have shown themselves to be inadequate for processing highly-ambiguous large-vocabulary text, as is evidenced by their poor per- formance on domains like the Wall Street Journal, and by the movement away from parsing-based approaches to textprocessing in general. In this paper, I describe SPATTER, a statistical parser based on decision-tree learning techniques which constructs a complete parse for every sentence and achieves accuracy rates far better than any published result. This work is based on the following premises: (1) grammars are too complex and detailed to develop manually for most interesting domains; (2) parsing models must rely heavily on lexical and contextual information to analyze sentences accurately; and (3) existing n-gram modeling techniques are inadequate for parsing models. In experiments comparing SPATTER with IBM's computer manuals parser, SPATTER significantly outperforms the grammar-based parser. Evaluating SPATTER against the Penn Treebank Wall Street Journal corpus using the PARSEVAL measures, SPATTER achieves 86% precision, 86% recall, and 1.3 crossing brackets per sentence for sentences of 40 words or less, and 91% precision, 90% recall, and 0.5 crossing brackets for sentences between 10 and 20 words in length.

this paper methods for constructing statistical models for computing the probability of attachment decisions. These models could be then integrated into scoring the probability of an overall parse. We present our methods in the context of prepositional phrase (PP) attachment.

We present a critical overview of the state-of-the-art in parser evaluation methodologies and metrics. A discussion of their relative strengths and weaknesses motivates a new---and we claim more informative and generally applicable---technique of measuring parser accuracy, based on the use of grammatical relations. We conclude with some preliminary results of experiments in which we use this new scheme to evaluate a robust parser of English.

Probabilistic Context-Free Grammars (PCFGs) and variations on them have recently become some of the most common formalisms for parsing. It is common with PCFGs to compute the inside and outside probabilities. When these probabilities are multiplied together and normalized, they produce the probability that any given non-terminal covers any piece of the input sentence. The traditional use of these probabilities is to improve the probabilities of grammar rules. In this thesis we show that these values are useful for solving many other problems in Statistical Natural Language Processing. We give a framework for describing parsers. The framework generalizes the inside and outside values to semirings. It makes it easy to describe parsers that compute a wide variety of interesting quantities, including the inside and outside probabilities, as well as related quantities such as Viterbi probabilities and n-best lists. We also present three novel uses for the inside and outside probabilities. T...

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Contents 1 Spoken Language Input 1 Ron Cole & Victor Zue, chapter editors 1.1 Overview : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 Victor Zue & Ron Cole 1.2 Speech Recognition : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 Victor Zue, Ron Cole, & Wayne Ward 1.3 Signal Representation : : : : : : : : : : : : : : : : : : : : : : : : : : 11 Melvyn J. Hunt 1.4 Robust Speech Recognition : : : : : : : : : : : : : : : : : : : : : : 17 Richard M. Stern 1.5 HMM Methods in Speech Recognition : : : : : : : : : : : : : : : 24 Renato De Mori & Fabio Brugnara 1.6 Language Representation : : : : : : : : : : : : : : : : : : : : : : : : 35 Salim Roukos 1.7 Speaker Recognition : : : : : : : : : : : : : : : : : : : : : : : : : : :<F35.37

Parser development is generally viewed as a primarily linguistic enterprise. A grammarian examines sentences, skillfully extracts the linguistic generalizations evident in the data, and writes grammar rules which cover the language. The grammarian

We present a new formalism, probabilistic feature grammar (PFG). PFGs combine most of the best properties of several other formalisms, including those of Collins, Magerman, and Charniak, and in experiments have comparable or better performance. PFGs generate features one at a time, probabilistically, conditioning the probabilities of each feature on other features in a local context. Because the conditioning is local, efficient polynomial time parsing algorithms exist for computing inside, outside, and Viterbi parses. PFGs can produce probabilities of strings, making them potentially useful for language modeling. Precision and recall results are comparable to the state of the art with words, and the best reported without words. 1 Introduction Recently, many researchers have worked on statistical parsing techniques which try to capture additional context beyond that of simple probabilistic context-free grammars (PCFGs), including Magerman (1995), Charniak (1996), Collins (1996; 1997), ...

Two apparently opposing DOP models exist in the literature: one which computes the parse tree involving the most frequent subtrees from a treebank and one which computes the parse tree involving the fewest subtrees from a treebank. This paper proposes an integration of the two models which outperforms each of them separately. Together with a PCFGreduction of DOP we obtain improved accuracy and efficiency on the Wall Street Journal treebank. Our results show an 11% relative reduction in error rate over previous models, and an average processing time of 3.6 seconds per WSJ sentence.

Introduction to Evaluation Terminology and Use We can broadly distinguish three kinds of evaluation, appropriate to three different goals. 1. Adequacy Evaluation This is determination of the fitness of a system for a purpose---will it do what is required, how well, at what cost, etc. Typically for a prospective user, it may be comparative or not, and may require considerable work to identify a user's needs. One model is consumer organizations which publish the results of tests on, e.g., cars or appliances, and identify best buys for certain price-performance targets. This also goes by the names evaluation and evaluation proper. 476 Chapter 13: Evaluation 2. Diagnostic Evaluation This is production of a system performance profile with respect to some taxonimization of the space of possible inputs. It is typically used by system developers, but sometimes offered to end-us