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A Maximum-Entropy-Inspired Parser
TLDR
A new parser for parsing down to Penn tree-bank style parse trees that achieves 90.1% average precision/recall for sentences of length 40 and less and 89.5% when trained and tested on the previously established sections of the Wall Street Journal treebank is presented. Expand
Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking
TLDR
This paper describes a simple yet novel method for constructing sets of 50- best parses based on a coarse-to-fine generative parser that generates 50-best lists that are of substantially higher quality than previously obtainable. Expand
Statistical Parsing with a Context-Free Grammar and Word Statistics
TLDR
A parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence that outperforms previous schemes is described. Expand
Effective Self-Training for Parsing
We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possibleExpand
Statistical language learning
TLDR
Eugene Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages and is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning. Expand
Tree-Bank Grammars
TLDR
This paper presents results on a tree-bank grammar based on the Penn WaII Street Journal tree bank that outperforms other non-word-based statistical parsers/grammars on this corpus and outperforms parsers that consider the input as a string of tags and ignore the actual words of the corpus. Expand
Association for Computational Linguistics
Permission to publish the above work (including, without limitation, the right to publish the work in whole or in part in any and all forms and media, now or hereafter known) is hereby granted to theExpand
Bayesian Networks without Tears
TLDR
An introduction to Bayesian networks for AI researchers with a limited grounding in probability theory is given, to make Bayesian Networks more accessible to the probabilistically unsophisticated. Expand
Finding Parts in Very Large Corpora
TLDR
A method for extracting parts of objects from wholes using a very large corpus with 55% accuracy for the top 50 words as ranked by the system is presented. Expand
Introduction to artificial intelligence
This book is an introduction on artificial intelligence. Topics include reasoning under uncertainty, robot plans, language understanding, and learning. The history of the field as well asExpand
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