27,953 research outputs found
Probabilistic Parsing Strategies
We present new results on the relation between purely symbolic context-free
parsing strategies and their probabilistic counter-parts. Such parsing
strategies are seen as constructions of push-down devices from grammars. We
show that preservation of probability distribution is possible under two
conditions, viz. the correct-prefix property and the property of strong
predictiveness. These results generalize existing results in the literature
that were obtained by considering parsing strategies in isolation. From our
general results we also derive negative results on so-called generalized LR
parsing.Comment: 36 pages, 1 figur
Edge-Based Best-First Chart Parsing
Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged 'best' by some probabilistic figure of merit (FOM). Recent work has used proba- bilistic context-free grammars (PCFGs) to sign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to us- ing a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finergrained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results -- that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
Evaluating two methods for Treebank grammar compaction
Treebanks, such as the Penn Treebank, provide a basis for the automatic creation of broad coverage grammars. In the simplest case, rules can simply be āread offā the parse-annotations of the corpus, producing either a simple or probabilistic context-free grammar. Such grammars, however, can be very large, presenting problems for the subsequent computational costs of parsing under the grammar.
In this paper, we explore ways by which a treebank grammar can be reduced in size or ācompactedā, which involve the use of two kinds of technique: (i) thresholding of rules by their number of occurrences; and (ii) a method of rule-parsing, which has both probabilistic and non-probabilistic variants. Our results show that by a combined use of these two techniques, a probabilistic context-free grammar can be reduced in size by 62% without any loss in parsing performance, and by 71% to give a gain in recall, but some loss in precision
Preparing, restructuring, and augmenting a French treebank: lexicalised parsers or coherent treebanks?
We present the Modified French Treebank (MFT), a completely revamped French Treebank, derived from the Paris 7 Treebank
(P7T), which is cleaner, more coherent, has several transformed structures, and introduces new linguistic analyses. To determine the effect of these changes, we
investigate how theMFT fares in statistical parsing. Probabilistic parsers trained on the MFT training set (currently 3800 trees) already perform better than their counterparts trained on five times the P7T data (18,548 trees), providing an extreme example of the importance of data quality over quantity in statistical parsing. Moreover,
regression analysis on the learning curve of parsers trained on the MFT lead to the prediction that parsers trained on the full projected 18,548 tree MFT training set
will far outscore their counterparts trained on the full P7T. These analyses also show how problematic data can lead to problematic conclusionsāin particular, we find that
lexicalisation in the probabilistic parsing of French is probably not as crucial as was once thought (Arun and Keller (2005))
Dependency parsing of Turkish
The suitability of different parsing methods for different languages is an important topic in
syntactic parsing. Especially lesser-studied languages, typologically different from the languages
for which methods have originally been developed, poses interesting challenges in this respect.
This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative
free constituent order language that can be seen as the representative of a wider class
of languages of similar type. Our investigations show that morphological structure plays an
essential role in finding syntactic relations in such a language. In particular, we show that
employing sublexical representations called inflectional groups, rather than word forms, as the
basic parsing units improves parsing accuracy. We compare two different parsing methods, one
based on a probabilistic model with beam search, the other based on discriminative classifiers and
a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless
of parsing method.We examine the impact of morphological and lexical information in detail and
show that, properly used, this kind of information can improve parsing accuracy substantially.
Applying the techniques presented in this article, we achieve the highest reported accuracy for
parsing the Turkish Treebank
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