Archives for 2000

Today I read a paper titled “Events in Property Patterns”

The abstract is:
A pattern-based approach to the presentation, codification and reuse of property specifications for finite-state verification was proposed by Dwyer and his collegues.

The patterns enable non-experts to read and write formal specifications for realistic systems and facilitate easy conversion of specifications between formalisms, such as LTL, CTL, QRE.

In this paper, we extend the pattern system with events – changes of values of variables in the context of LTL.

Today I read a paper titled “Towards History-based Grammars: Using Richer Models for Probabilistic Parsing”

The abstract is:
We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity.

HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way.

We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence.

This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse.

In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error..

Today I read a paper titled “Temporal Relations: Reference or Discourse Coherence?”

The abstract is:
The temporal relations that hold between events described by successive utterances are often left implicit or underspecified.

We address the role of two phenomena with respect to the recovery of these relations: (1) the referential properties of tense, and (2) the role of temporal constraints imposed by coherence relations.

We account for several facets of the identification of temporal relations through an integration of these..

Today I read a paper titled “Look-Back and Look-Ahead in the Conversion of Hidden Markov Models into Finite State Transducers”

The abstract is:
This paper describes the conversion of a Hidden Markov Model into a finite state transducer that closely approximates the behavior of the stochastic model.

In some cases the transducer is equivalent to the HMM.

This conversion is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors.

The speed of tagging is also improved.

The described methods have been implemented and successfully tested..