Providing a Unified Account of Definite Noun Phrases in Discourse

Linguistic theories typically assign various linguistic phenomena to one of the categories, syntactic, semantic, or pragmatic, as if the phenomena in each category were relatively independent of those in the others. However, various phenomena in discourse do not seem to yield comfortably to any account that is strictly a syntactic or semantic or pragmatic one. This paper focuses on particular phenomena of this sort-the use of various referring expressions such as definite noun phrases and pronouns-and examines their interaction with mechanisms used to maintain discourse coherence.Engineering and Applied Science

Using Decision Trees for Coreference Resolution

This paper describes RESOLVE, a system that uses decision trees to learn how to classify coreferent phrases in the domain of business joint ventures. An experiment is presented in which the performance of RESOLVE is compared to the performance of a manually engineered set of rules for the same task. The results show that decision trees achieve higher performance than the rules in two of three evaluation metrics developed for the coreference task. In addition to achieving better performance than the rules, RESOLVE provides a framework that facilitates the exploration of the types of knowledge that are useful for solving the coreference problem.Comment: 6 pages; LaTeX source; 1 uuencoded compressed EPS file (separate); uses ijcai95.sty, named.bst, epsf.tex; to appear in Proc. IJCAI '9

Incremental Centering and Center Ambiguity

In this paper, we present a model of anaphor resolution within the framework of the centering model. The consideration of an incremental processing mode introduces the need to manage structural ambiguity at the center level. Hence, the centering framework is further refined to account for local and global parsing ambiguities which propagate up to the level of center representations, yielding moderately adapted data structures for the centering algorithm.Comment: 6 pages, uuencoded gzipped PS file (see also Technical Report at: http://www.coling.uni-freiburg.de/public/papers/cogsci96-center.ps.gz

La interfaz entre prosodia y discurso en la resolución de la anáfora pronominal en español

El estudio de la interfaz entre la prosodia y el discurso oral en español se ha centrado en aspectos como la caracterización prosódica de los marcadores discursivos, el análisis de las funciones de la entonación en la conversación coloquial o la descripción de los correlatos prosódicos de la cortesía y de la ironía, por mencionar únicamente algunos de los temas que han merecido más atención entre los investigadores. Sin embargo, el papel de la información prosódica en la resolución de la anáfora pronominal en el discurso oral en español no parece que se haya abordado todavía en profundidad, pese a que los problemas relacionados con la interpretación de los elementos anafóricos ocupan un lugar prominente en el ámbito de la investigación sobre el discurso, tanto desde la perspectiva de la teoría lingüística como desde el punto de vista de la lingüística computacional (Rello 2010: 5-19). Por otra parte, para el desarrollo de los sistemas de diálogo ―una de las principales aplicaciones de las tecnologías del habla en la actualidad- es fundamental realizar un trata-miento adecuado de la anáfora, combinando los métodos propios del procesamiento del lenguaje natural con la extracción, mediante técnicas de reconocimiento automático del habla, de los parámetros acústicos relacionados con la prosodia

Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application

We present two novel models of document coherence and their application to information retrieval (IR). Both models approximate document coherence using discourse entities, e.g. the subject or object of a sentence. Our first model views text as a Markov process generating sequences of discourse entities (entity n-grams); we use the entropy of these entity n-grams to approximate the rate at which new information appears in text, reasoning that as more new words appear, the topic increasingly drifts and text coherence decreases. Our second model extends the work of Guinaudeau & Strube [28] that represents text as a graph of discourse entities, linked by different relations, such as their distance or adjacency in text. We use several graph topology metrics to approximate different aspects of the discourse flow that can indicate coherence, such as the average clustering or betweenness of discourse entities in text. Experiments with several instantiations of these models show that: (i) our models perform on a par with two other well-known models of text coherence even without any parameter tuning, and (ii) reranking retrieval results according to their coherence scores gives notable performance gains, confirming a relation between document coherence and relevance. This work contributes two novel models of document coherence, the application of which to IR complements recent work in the integration of document cohesiveness or comprehensibility to ranking [5, 56]