一个强大的语言平台，用于高效和特定领域的Web内容分析
A Robust Linguistic Platform for Efficient and Domain specific Web Content Analysis
摘要：
特定领域的Web语义访问要求具有增强的语义查询和索引能力的专用搜索引擎，其涉及信息检索（IR）和信息抽取（IE）。需要丰富的语言分析来识别相关的语义单元以根据语言特定的统计分布对其进行索引和加权，或者作为信息抽取过程的基础。最近的发展使得自然语言处理（NLP）技术足够可靠，可以处理大量的文档，并使用语义注释来丰富它们。本文主要介绍了ALVIS项目中的一个文本处理平台Ogmios的设计与开发。Ogmios平台利用现有的NLP模块和资源，这些模块和资源可以针对特定领域进行调整，并生成语言注释的文档。我们展示了如何通用性，域语义意识和性能的三个约束可以一起处理。
英文摘要：
Web semantic access in specific domains calls for specialized search engines with enhanced semantic querying and indexing capacities, which pertain both to information retrieval (IR) and to information extraction (IE). A rich linguistic analysis is required either to identify the relevant semantic units to index and weight them according to linguistic specific statistical distribution, or as the basis of an information extraction process. Recent developments make Natural Language Processing (NLP) techniques reliable enough to process large collections of documents and to enrich them with semantic annotations. This paper focuses on the design and the development of a text processing platform, Ogmios, which has been developed in the ALVIS project. The Ogmios platform exploits existing NLP modules and resources, which may be tuned to specific domains and produces linguistically annotated documents. We show how the three constraints of genericity, domain semantic awareness and performance can be handled all together.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
