383 research outputs found
Learning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities
This paper presents the results of a study on the semantic constraints
imposed on lexical choice by certain contextual indicators. We show how such
indicators are computed and how correlations between them and the choice of a
noun phrase description of a named entity can be automatically established
using supervised learning. Based on this correlation, we have developed a
technique for automatic lexical choice of descriptions of entities in text
generation. We discuss the underlying relationship between the pragmatics of
choosing an appropriate description that serves a specific purpose in the
automatically generated text and the semantics of the description itself. We
present our work in the framework of the more general concept of reuse of
linguistic structures that are automatically extracted from large corpora. We
present a formal evaluation of our approach and we conclude with some thoughts
on potential applications of our method.Comment: 7 pages, uses colacl.sty and acl.bst, uses epsfig. To appear in the
Proceedings of the Joint 17th International Conference on Computational
Linguistics 36th Annual Meeting of the Association for Computational
Linguistics (COLING-ACL'98
Parallel J-W Monte Carlo Simulations of Thermal Phase Changes in Finite-size Systems
The thermodynamic properties of 59 TeF6 clusters that undergo
temperature-driven phase transitions have been calculated with a canonical
J-walking Monte Carlo technique. A parallel code for simulations has been
developed and optimized on SUN3500 and CRAY-T3E computers. The Lindemann
criterion shows that the clusters transform from liquid to solid and then from
one solid structure to another in the temperature region 60-130 K.Comment: 4 pages, 5 figures; presented at the conference on computational
physics, Aachen (2001) accepted for publication in Comp.Phys.Com
Building a Generation Knowledge Source using Internet-Accessible Newswire
In this paper, we describe a method for automatic creation of a knowledge
source for text generation using information extraction over the Internet. We
present a prototype system called PROFILE which uses a client-server
architecture to extract noun-phrase descriptions of entities such as people,
places, and organizations. The system serves two purposes: as an information
extraction tool, it allows users to search for textual descriptions of
entities; as a utility to generate functional descriptions (FD), it is used in
a functional-unification based generation system. We present an evaluation of
the approach and its applications to natural language generation and
summarization.Comment: 8 pages, uses eps
Use of Weighted Finite State Transducers in Part of Speech Tagging
This paper addresses issues in part of speech disambiguation using
finite-state transducers and presents two main contributions to the field. One
of them is the use of finite-state machines for part of speech tagging.
Linguistic and statistical information is represented in terms of weights on
transitions in weighted finite-state transducers. Another contribution is the
successful combination of techniques -- linguistic and statistical -- for word
disambiguation, compounded with the notion of word classes.Comment: uses psfig, ipamac
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization
We introduce a stochastic graph-based method for computing relative
importance of textual units for Natural Language Processing. We test the
technique on the problem of Text Summarization (TS). Extractive TS relies on
the concept of sentence salience to identify the most important sentences in a
document or set of documents. Salience is typically defined in terms of the
presence of particular important words or in terms of similarity to a centroid
pseudo-sentence. We consider a new approach, LexRank, for computing sentence
importance based on the concept of eigenvector centrality in a graph
representation of sentences. In this model, a connectivity matrix based on
intra-sentence cosine similarity is used as the adjacency matrix of the graph
representation of sentences. Our system, based on LexRank ranked in first place
in more than one task in the recent DUC 2004 evaluation. In this paper we
present a detailed analysis of our approach and apply it to a larger data set
including data from earlier DUC evaluations. We discuss several methods to
compute centrality using the similarity graph. The results show that
degree-based methods (including LexRank) outperform both centroid-based methods
and other systems participating in DUC in most of the cases. Furthermore, the
LexRank with threshold method outperforms the other degree-based techniques
including continuous LexRank. We also show that our approach is quite
insensitive to the noise in the data that may result from an imperfect topical
clustering of documents
INFLUENCE OF SOME EXPERIMENTAL PARAMETERS ON THE RELATION BETWEEN THE REACTION TIME AND THE AMPLITUDE CHANGES OF THE H-REFLEX
No abstrac
EFFECT OF PROTEIN HYDROLYSATE, ALVESIN AND PHYSIOLOGICAL SALINE ON GLYCEMIA IN EXPERIMENTAL DIABETES CAUSED BY STREPTOSOTOCIN
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