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Titlebook: Cross-Language Information Retrieval and Evaluation; Workshop of Cross-La Carol Peters Conference proceedings 2001 Springer-Verlag Berlin H

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樓主: Fillmore
41#
發(fā)表于 2025-3-28 17:50:21 | 只看該作者
CLEF 2000 — Overview of Results the number of participants and of experiments has grown considerably. This paper presents details of the various subtasks, and attempts to summarize the main results and research directions that were observed. Additionally, the CLEF collection is examined with respect to the completeness of its rel
42#
發(fā)表于 2025-3-28 19:07:30 | 只看該作者
Translation Resources, Merging Strategies, and Relevance Feedback for Cross-Language Information Retngual and multilingual tasks. The following new techniques are introduced in this paper. In the bilingual task we experimented with different methods to estimate translation probabilities. In the multilingual task we experimented with refinements on raw-score merging techniques and with a new releva
43#
發(fā)表于 2025-3-29 01:32:11 | 只看該作者
44#
發(fā)表于 2025-3-29 03:42:57 | 只看該作者
A Language-Independent Approach to European Text Retrievalr thesauri. Using the HAIRCUT system we participated in the monolingual, bilingual, and multilingual tasks of the CLEF-2000 evaluation. Our approach, based on combining the benefits of words and character n-grams, was effective for both language-independent monolingual retrieval as well as for cross
45#
發(fā)表于 2025-3-29 10:36:07 | 只看該作者
46#
發(fā)表于 2025-3-29 15:10:07 | 只看該作者
47#
發(fā)表于 2025-3-29 16:23:26 | 只看該作者
48#
發(fā)表于 2025-3-29 22:09:08 | 只看該作者
The Use of NLP Techniques in CLIRage information retrieval (clir) linguistic processing is already well established. In this paper, the clir component - .-IR - which is presented has been developed as the core module of a multilingual information system in a legal domain. This component uses not only the lexical base form for index
49#
發(fā)表于 2025-3-30 00:05:25 | 只看該作者
50#
發(fā)表于 2025-3-30 05:34:41 | 只看該作者
Multilingual Information Retrieval Based on Parallel Texts from the Webranslation. Some of the models are trained on parallel web pages that are automatically mined from the Web. Others are trained from bilingual dictionaries and lexical databases. These models are combined in query translation. Our goal in this series of experiments is to test if the parallel web page
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