Archives for 2000

Today I read a paper titled “Priority Union and Generalization in Discourse Grammars”

The abstract is:
We describe an implementation in Carpenter’s typed feature formalism, ALE, of a discourse grammar of the kind proposed by Scha, Polanyi, et al.

We examine their method for resolving parallelism-dependent anaphora and show that there is a coherent feature-structural rendition of this type of grammar which uses the operations of priority union and generalization.

We describe an augmentation of the ALE system to encompass these operations and we show that an appropriate choice of definition for priority union gives the desired multiple output for examples of VP-ellipsis which exhibit a strict/sloppy ambiguity..

Today I read a paper titled “Lifeworld Analysis”

The abstract is:
We argue that the analysis of agent/environment interactions should be extended to include the conventions and invariants maintained by agents throughout their activity.

We refer to this thicker notion of environment as a lifeworld and present a partial set of formal tools for describing structures of lifeworlds and the ways in which they computationally simplify activity.

As one specific example, we apply the tools to the analysis of the Toast system and show how versions of the system with very different control structures in fact implement a common control structure together with different conventions for encoding task state in the positions or states of objects in the environment.

Today I read a paper titled “A Maximum-Entropy Partial Parser for Unrestricted Text”

The abstract is:
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.

Today I read a paper titled “Similarity-Based Estimation of Word Cooccurrence Probabilities”

The abstract is:
In many applications of natural language processing it is necessary to determine the likelihood of a given word combination.

For example, a speech recognizer may need to determine which of the two word combinations “eat a peach” and “eat a beach” is more likely.

Statistical NLP methods determine the likelihood of a word combination according to its frequency in a training corpus.

However, the nature of language is such that many word combinations are infrequent and do not occur in a given corpus.

In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on “most similar” words.

We describe a probabilistic word association model based on distributional word similarity, and apply it to improving probability estimates for unseen word bigrams in a variant of Katz’s back-off model.

The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error..