Abstract not found

Département d’informatique et de recherche opérationnelle

In this paper, we introduce a new framework for recognizing textual entailment which depends on extraction of the set of publiclyheld beliefs – known as discourse commitments – that can be ascribed to the author of a text or a hypothesis. Once a set of commitments have been extracted from a t-h pair, the task of recognizing textual entailment is reduced to the identification of the commitments from a t which support the inference of the h. Promising results were achieved: our system correctly identified more than 80 % of examples from the RTE-3 Test Set correctly, without the need for additional sources of training data or other web-based resources. 1

For the 11th straight year, the Conference on Computational Natural Language Learning has been accompanied by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2009, the shared task was dedicated to the joint parsing of syntactic and semantic dependencies in multiple languages. This shared task combines the shared tasks of the previous five years under a unique dependency-based formalism similar to the 2008 task. In this paper, we define the shared task, describe how the data sets were created and show their quantitative properties, report the results and summarize the approaches of the participating systems.

This paper surveys work on applying the insights of lexicalized grammars to low-level discourse, to show the value of positing an autonomous grammar for low-level discourse in which words (or idiomatic phrases) are associated with discourse-level predicate–argument structures or modification structures that convey their syntactic-semantic meaning and scope. It starts by describing a lexicalized Tree Adjoining Grammar for discourse (D-LTAG). It then reviews an initial experiment in parsing text automatically, using both a lexicalized TAG and D-LTAG, and then touches upon issues involved in how lexico-syntactic elements contribute to discourse semantics. The paper concludes with a brief description of the Penn Discourse TreeBank, a resource being developed for the study of discourse structure and semantics.

We present a sentence extraction algorithm for Information Distillation, a task where for a given templated query, relevant passages must be extracted from massive audio and textual document sources. For each sentence of the relevant documents (that are assumed to be known from the upstream stages) we employ statistical classification methods to estimate the extent of its relevance to the query, whereby two aspects of relevance are taken into account: the template (type) of the query and its slots (free-text descriptions of names, organizations, topic, events and so on, around which templates are centered). The idiosyncrasy of the presented method is in the choice of features used for classification. We extract our features from charts, compilations of elements from various annotation levels, such as word transcriptions, syntactic and semantic parses, and Information Extraction annotations. In our experiments we show that this integrated approach outperforms a purely lexical baseline by as much as 30 % relative in terms of F-measure. We also investigate the algorithm’s behavior under noisy conditions, by comparing its performance on ASR output and on corresponding manual transcriptions.

In this paper, we discuss how to annotate coreference and predicate-argument relations in Japanese written text. There have been research activities for building Japanese text corpora annotated with coreference and predicate-argument relations as are done in the Kyoto Text Corpus version 4.0 (Kawahara et al., 2002) and the GDA-Tagged Corpus (Hasida, 2005). However, there is still much room for refining their specifications. For this reason, we discuss issues in annotating these two types of relations, and propose a new specification for each. In accordance with the specification, we built a large-scaled annotated corpus, and examined its reliability. As a result of our current work, we have released an annotated corpus named the NAIST Text Corpus1, which is used as the evaluation data set in the coreference and zero-anaphora resolution tasks in Iida et al. (2005) and Iida et al. (2006). 1

In this paper, we describe the Argument Selection and Coercion task, currently in development for the SemEval-2 evaluation exercise scheduled for 2010. This task involves characterizing the type of compositional operation that exists between a predicate and the arguments it selects. Specifically, the goal is to identify whether the type that a verb selects is satisfied directly by the argument, or whether the argument must change type to satisfy the verb typing. We discuss the problem in detail and describe the data preparation for the task. 1

Recent work on semantic role labeling (SRL) has focused almost exclusively on the analysis of the predicate-argument structure of verbs, largely due to the lack of human-annotated resources for other types of predicates that can serve as training and test data for the semantic role labeling systems. However, it is wellknown that verbs are not the only type of predicates that can take arguments. Most notably, nouns that are nominalized forms of verbs and relational nouns generally are also considered to have their own predicate-argument structure. In this paper we report results of SRL experiments on nominalized predicates in Chinese, using a newly completed corpus, the Chinese Nombank. We also discuss the impact of using publicly available manually annotated verb data to improve the SRL accuracy of nouns, exploiting a widely-held assumption that verbs and their nominalizations share the same predicate-argument structure. Finally, we discuss the results of applying reranking techniques to improve SRL accuracy for nominalized predicates, which showed insignificant improvement. 1

An interlingua is a notation for representing the content of a text that abstracts away from the characteristics of the language itself and focuses on the meaning (semantics) alone. Interlinguas are typically used as pivot representations in machine translation, allowing the contents of a source text to be generated in many different target languages. Due to the complexities involved, few interlinguas are more than demonstration prototypes, and only one has been used in a commercial MT system. In this article we define the components of an interlingua and the principal issues faced by designers and builders of interlinguas and interlingua MT systems, illustrating with examples from operational systems and research prototypes. We discuss current efforts to annotate texts with interlingua-based information.