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Natural
Language Processing
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Computer
Science > Natural Language Processing
> Speaker Bureau
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| Speaker Bureau | |
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Many of our Natural Language Processing researchers are available to present their work at your site. |
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Abstracts for Talks
- Natural Language Understanding in a restricted domain can be posed as problem of selecting the most probable formal language sentence given the natural language utterance. Often, a finite set of formal language sentences cover the intended domain very well. Then, NLU involves building a discrete conditional probability distribution on the finite set of formal sentences, conditioned on the natural language utterance. We discuss building this a posteriori model directly using maximum entropy and related frameworks. Results will be presented in the Air Travel Information Service domain.
Direct Translation Models for Natural Language Understanding, Kishore Papineni
- Inverse
Document Frequency is I-Divergence Optimal, Kishore
Papineni
Inverse Document Frequency (IDF), widely used in information retrieval, is a popular measure of a word's importance. While its use is justified in many ways, it has not been shown to be optimal in a formal framework. In this talk, we show that IDF is the optimal weight of a word with respect to I-Divergence in an information retrieval setting assuming that relevant documents contain all query terms. We also assign optimal weights to longer n-grams, with the weights seen as a natural extension to IDF of a single word. We use this framework for unsupervised identification of phrases in text corpora.
Complexity of Natural Language, Wlodek Zadrozny
Is it possible to estimate the difficulty of creating natural language applications? This problem is of growing practical and theoretical interest. One measure of complexity is the number of objects and relations in the domain of consideration. However, for practical applications, measures of complexity must take into account accuracy of processing, relationship to world knowledge, evaluations etc. The talk surveys initial results and describes research issues ranging from purely mathematical and linguistic to technological.
Natural Language Semantics, Wlodek Zadrozny
We look at formal relations between synonymy and compositionality. We prove that synomy does not put any formal constraints on compositionality. i.e. with a proper encoding,if two expressions are synonymous, then their compositional meanings are identical. We suggest that the reason that semanticists have been anxious to preserve compositionality as a significant constraint on semantic theory is that it has been mistakenly regarded as a condition that must be satisfied by any theory that sustains a systematic connection between the meaning of an expression and the meanings of its parts. Recent developments in formal and computational semantics show that systematic theories of meanings need not be compositional (joint work w. Shalom Lappin, King's College).
- Statistical Parser Adaptation Based on Model Transform, Xiaoqiang Luo
- It is observed that performance of a statistical parser degrades rapidly when the style of test text is different from training text. I will talk about how parsing accuracy can be improved by transforming models. Two types of transforms have been explored: 1) using a Markov matrix; 2) using Householder transform. Results show that classing errors can be reduced by 20-30% in AirTrav domain.
- Statistical
Parser Adaptation Via Householder Transform,
Xiaoqiang Luo
We propose a method of adapting a statistical parser using a special orthogonal transform -- Householder transform. Probability mass functions (pmf) in the parser are first mapped to unit sphere, then a Householder transform is applied, which maps a point in unit sphere to another point in unit sphere. The final model is obtained by mapping the transformed point in unit sphere back to simplex through a square map. The proposed method is tested on a semantic parser, and over 20% relative reduction of parse errors can be achieved.
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Estimating the Probabilities of Unseen Events: A Language Modeling Perspective, Stanley Chen
- In many tasks, we must estimate the probabilities of events that we have never seen before. For example, consider the estimation of the probability that the next president of the U.S. is a woman. A naive estimate can be found by dividing the number of past female presidents by the total number of past presidents, but the resulting estimate of zero is clearly an underestimate. A wide range of techniques have been developed in the field of "language modeling" to improve on these naive "maximum likelihood" estimates. Language modeling deals with the estimation of the frequency of a given word given the preceding words in text. In this talk, we present a history of the "smoothing" techniques studied in language modeling to estimate unseen event probabilities, and we discuss the impact of improved smoothing in language modeling on the tasks of text compression and speech recognition.
- Tractable Representations of Finite Distributive Lattices, Frank J. Oles
- A goal of this talk is to show how ideas from lattice theory can be used in the implementation of a knowledge representation language. First, the semantics of a simple knowledge representation language is presented. Then we show how to use Birkhoff's Representation Theorem for Finite Distributive Lattices to build incrementally what we call a Birkhoff implementation of a knowledge base by processing a sequence of terminological axioms. A mathematical proof of the correctness of our technique with respect to the given semantics is an integral part of the development. While the intended application is to knowledge representation, these methods can be used whenever a computationally tractable representation of a finite distributive lattice needs to be implemented, such as when an ontology is required for natural language processing.
