Stefan Riezler

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We present a stochastic parsing system consisting of a Lexical-Functional Grammar (LFG), a constraint-based parser and a stochastic disambiguation model. We report on the results of applying this system to parsing the UPenn Wall Street Journal (WSJ) treebank. The model combines full and partial parsing techniques to reach full grammar coverage on unseen(More)
Log-linear models provide a statistically sound framework for Stochastic “Unification-Based” Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe two computationally-tractable ways of estimating the parameters of such grammars from a training corpus of syntactic analyses, and apply these to estimate a stochastic version of(More)
We present a technique for automatic induction of slot annotations for subcategorization frames, based on induction of hidden classes in the EM framework of statistical estimation. The models are empirically evalutated by a general decision test. Induction of slot labeling for subcategorization frames is accomplished by a further application of EM, and(More)
This paper reports some experiments that compare the accuracy and performance of two stochastic parsing systems. The currently popular Collins parser is a shallow parser whose output contains more detailed semanticallyrelevant information than other such parsers. The XLE parser is a deep-parsing system that couples a Lexical Functional Grammar to a(More)
In this paper we discuss the construction, features, and current uses of the PARC 700 DEPBANK. The PARC 700 DEPBANK is a dependency bank containing predicate-argument relations and a wide variety of other grammatical features. It was semi-automatically produced and boot-strapped from the output of a deep parser: this allowed for greater consistency of(More)
We present an approach to query expansion in answer retrieval that uses Statistical Machine Translation (SMT) techniques to bridge the lexical gap between questions and answers. SMT-based query expansion is done by i) using a full-sentence paraphraser to introduce synonyms in context of the entire query, and ii) by translating query terms into answer terms(More)
We present an application of ambiguity packing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation. Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output(More)
We investigate some pitfalls regarding the discriminatory power of MT evaluation metrics and the accuracy of statistical significance tests. In a discriminative reranking experiment for phrase-based SMT we show that the NIST metric is more sensitive than BLEU or F-score despite their incorporation of aspects of fluency or meaning adequacy into MT(More)
This paper reports on the use of two distinct evaluation metrics for assessing a stochastic parsing model consisting of a broad-coverage Lexical-Functional Grammar (LFG), an efficient constraint-based parser and a stochastic disambiguation model. The first evaluation metric measures matches of predicate-argument relations in LFG f-structures (henceforth the(More)
With a few exceptions, discriminative training in statistical machine translation (SMT) has been content with tuning weights for large feature sets on small development data. Evidence from machine learning indicates that increasing the training sample size results in better prediction. The goal of this paper is to show that this common wisdom can also be(More)