34 research outputs found
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
How to Evaluate your Question Answering System Every Day and Still Get Real Work Done
In this paper, we report on Qaviar, an experimental automated evaluation
system for question answering applications. The goal of our research was to
find an automatically calculated measure that correlates well with human
judges' assessment of answer correctness in the context of question answering
tasks. Qaviar judges the response by computing recall against the stemmed
content words in the human-generated answer key. It counts the answer correct
if it exceeds agiven recall threshold. We determined that the answer
correctness predicted by Qaviar agreed with the human 93% to 95% of the time.
41 question-answering systems were ranked by both Qaviar and human assessors,
and these rankings correlated with a Kendall's Tau measure of 0.920, compared
to a correlation of 0.956 between human assessors on the same data.Comment: 6 pages, 3 figures, to appear in Proceedings of the Second
International Conference on Language Resources and Evaluation (LREC 2000
Viterbi Training for PCFGs: Hardness Results and Competitiveness of Uniform Initialization
We consider the search for a maximum likelihood assignment of hidden derivations and grammar weights for a probabilistic context-free grammar, the problem approximately solved by “Viterbi training.” We show that solving and even approximating Viterbi training for PCFGs is NP-hard. We motivate the use of uniformat-random initialization for Viterbi EM as an optimal initializer in absence of further information about the correct model parameters, providing an approximate bound on the log-likelihood.
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however
commonly used Markov models are unable to adequately describe these phenomena
due to their strong locality assumptions. We propose a novel hierarchical model
for structured prediction over sequences and trees which exploits global
context by conditioning each generation decision on an unbounded context of
prior decisions. This builds on the success of Markov models but without
imposing a fixed bound in order to better represent global phenomena. To
facilitate learning of this large and unbounded model, we use a hierarchical
Pitman-Yor process prior which provides a recursive form of smoothing. We
propose prediction algorithms based on A* and Markov Chain Monte Carlo
sampling. Empirical results demonstrate the potential of our model compared to
baseline finite-context Markov models on part-of-speech tagging and syntactic
parsing
Joint Morphological and Syntactic Disambiguation
In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a single problem? We describe several efficient, probabilistically interpretable ways to apply joint inference to morphological and syntactic disambiguation using lattice parsing. Joint inference is shown to compare favorably to pipeline parsing methods across a variety of component models. State-of-the-art performance on Hebrew Treebank parsing is demonstrated using the new method. The benefits of joint inference are modest with the current component models, but appear to increase as components themselves improve
Latent-Variable PCFGs: Background and Applications
Latent-variable probabilistic context-free grammars are
latent-variable models that are based on context-free grammars.
Nonterminals are associated with latent states that provide
contextual information during the top-down rewriting process of
the grammar.
We survey a few of the techniques used to estimate such grammars
and to parse text with them. We also give an overview of what the latent
states represent for English Penn treebank parsing, and provide
an overview of extensions and related models to these grammars