摘要翻译：
特定领域的Web语义访问要求具有增强语义查询和索引能力的专门搜索引擎，这既涉及信息检索(IR)也涉及信息抽取(IE)。丰富的语言分析既可以识别相关的语义单位，并根据语言的统计分布对其进行索引和加权，也可以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，可以处理大量文档，并用语义注释丰富它们。本文主要介绍了ALVIS项目中的文本处理平台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调整到特定的领域，并生成语言注释文档。我们展示了泛型、领域语义感知和性能这三个约束是如何一起处理的。
---
英文标题：
《A Robust Linguistic Platform for Efficient and Domain specific Web
  Content Analysis》
---
作者：
Thierry Hamon (LIPN), Adeline Nazarenko (LIPN), Thierry Poibeau
  (LIPN), Sophie Aubin (LIPN), Julien Derivi\`ere (LIPN)
---
最新提交年份：
2007
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
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.
---
PDF链接：
https://arxiv.org/pdf/0706.4375