|LREC 2000 2nd International Conference on Language Resources & Evaluation|
Papers and abstracts by paper title: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Papers and abstracts by ID number: 1-50, 51-100, 101-150, 151-200, 201-250, 251-300, 301-350, 351-377.
List of all papers and abstracts
|138||How To Evaluate and Compare Tagsets? A Proposal||We propose a methodology which allows an evaluation of distributional qualities of a tagset and a comparison between tagsets. Evaluation of tagset is crucial since the task of tagging is often considered as one of the first tasks in language processing. The aim of tagging is to summarise as well as possible linguistic information for further processing such as syntactic parsing. The idea is to consider these further steps in order to evaluate a given tagset, and thus to measure the pertinence of the information provided by the tagset for these steps. For this purpose, a Machine Learning system, ALLiS, is used, whose goal is to learn phrase structures from bracketed corpora and to generate formal grammar which describes these structures. ALLiS learning is based on the detection of structural regularities. By this means, it can be pointed out some non-distributional behaviours of the tagset, and thus some of its weaknesses or its inadequacies.|
|203||How to Evaluate Your Question Answering System Every Day ... and Still Get Real Work Done||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 against the stemmed content words in the human-generated answer key. It counts the answer correct if it exceeds a given recall threshold. We determined that the answer correctness predicted by Qaviar agreed with the human 93% to 95% of the time. 41 question-answering systems were ranked by both Qaviar and human assessors, and these rankings correlated with a Kendall’s Tau measure of 0.920, compared to a correlation of 0.956 between human assessors on the same data.|
|372||Hua Yu: A Word-segmented and Part-Of-Speech Tagged Chinese Corpus||As the outcome of a 3-year joint effort of Department of Computer Science, Tsinghua University and Language Information Processing Institute, Beijing Language and Culture University, Beijing, China, a word-segmented and part-of-speech tagged Chinese corpus with size of 2 million Chinese characters, named HuaYu, has been established. This paper firstly introduces some basics about HuaYu in brief, as its genre distribution, fundamental considerations in designing it, word segmentation and part-of-speech tagging standards. Then the complete list of tag set used in HuaYu is given, along with typical examples for each tag accordingly. Several pieces of annotated texts in each genre are also included at last for reader's reference.|