781 research outputs found
A Maximum-Entropy Partial Parser for Unrestricted Text
This paper describes a partial parser that assigns syntactic structures to
sequences of part-of-speech tags. The program uses the maximum entropy
parameter estimation method, which allows a flexible combination of different
knowledge sources: the hierarchical structure, parts of speech and phrasal
categories. In effect, the parser goes beyond simple bracketing and recognises
even fairly complex structures. We give accuracy figures for different
applications of the parser.Comment: 9 pages, LaTe
A PDTB-Styled End-to-End Discourse Parser
We have developed a full discourse parser in the Penn Discourse Treebank
(PDTB) style. Our trained parser first identifies all discourse and
non-discourse relations, locates and labels their arguments, and then
classifies their relation types. When appropriate, the attribution spans to
these relations are also determined. We present a comprehensive evaluation from
both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table
A Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars
The paper gives a brief review of the expectation-maximization algorithm
(Dempster 1977) in the comprehensible framework of discrete mathematics. In
Section 2, two prominent estimation methods, the relative-frequency estimation
and the maximum-likelihood estimation are presented. Section 3 is dedicated to
the expectation-maximization algorithm and a simpler variant, the generalized
expectation-maximization algorithm. In Section 4, two loaded dice are rolled. A
more interesting example is presented in Section 5: The estimation of
probabilistic context-free grammars.Comment: Presented at the 15th European Summer School in Logic, Language and
Information (ESSLLI 2003). Example 5 extended (and partially corrected
Wide-coverage deep statistical parsing using automatic dependency structure annotation
A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather than CFG-tree) representations
for parser evaluation. Preiss (2003) and Kaplan et al. (2004) conducted a number of experiments comparing ādeepā hand-crafted wide-coverage with āshallowā treebank- and machine-learning based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit the experiments
in Preiss (2003) and Kaplan et al. (2004), this time using the sophisticated automatic LFG f-structure annotation methodologies of Cahill et al. (2002b, 2004) and Burke (2006), with surprising results. We compare various PCFG and history-based parsers (based on Collins, 1999; Charniak, 2000; Bikel, 2002) to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers (Carroll and Briscoe 2002; Riezler et al. 2002). We evaluate using dependency-based gold standards (DCU 105, PARC 700, CBS 500 and dependencies for WSJ Section 22) and use the Approximate Randomization Test (Noreen 1989) to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, widecoverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank (King et al. 2003), a statistically significant improvement of 2.18%over the most recent results of 80.55%for the hand-crafted LFG grammar and XLE parsing system of Riezler et al. (2002), and an f-score of 80.23% against the CBS 500 Dependency Bank (Carroll, Briscoe, and Sanfilippo 1998), a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system of Carroll and
Briscoe (2002)
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