Learn More
This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2).1 We describe the RTE2 dataset and overview the submissions for the challenge. One of the main goals for this year’s dataset was to provide more “realistic” text-hypothesis examples, based mostly on outputs of actual systems. The 23 submissions for the challenge present(More)
We describe MITRE’s two submissions to the RTE Challenge, intended to exemplify two different ends of the spectrum of possibilities. The first submission is a traditional system based on linguistic analysis and inference, while the second is inspired by alignment approaches from machine translation. We also describe our efforts to build our own entailment(More)
In this paper, we report on Qaviar, an experimental automated evaluation system for question answering applications. The goal of our research was to find an automatically calculated measure that correlates well with human judges' assessment of answer correctness in the context of question answering tasks. Qaviar judges the response by computing recall(More)
This paper introduces a set of guidelines for annotating time expressions with a canonicalized representation of the times they refer to. Applications that can benefit from such an annotated corpus include information extraction (e.g., normalizing temporal references for database entry), question answering (answering “when” questions), summarization(More)
Broadcast news sources and newspapers provide society with the vast majority of real-time information. Unfortunately, cost efficiencies and real-time pressures demand that producers, editors, and writers select and organize content for stereotypical audiences. In this article we illustrate how content understanding, user modeling, and tailored presentation(More)
Appears in Computational Natural Language Learning (CoNLL-99), pages 43-52. A workshop at the 9th Conf. of the European Chapter of the Assoc. for Computational Linguistics (EACL-99). Bergen, Norway, June, 1999. cs.CL/9906015 Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these(More)