Learning Semantic Representations for Information Retrieval

小行星9221276 面向信息检索的语义表示学习 这是一个为期三年的连续奖励的第一年资助。 这个项目的目标是开发方法，以自动表示基于文本的文件，从一个大的集合的方式，便于语义精确检索。 表示文档的一个关键问题是文档中的单词不是文档内容的准确描述符。 这部分是由于自然语言的多义性：一个概念可以用许多不同的方式描述。 目前大多数的方法都没有考虑到这一点，因为它们使用文档中单词的共现来确定语义相关性。 这种方法是索引文档，以便当它们在语义上相关时，它们在表示上相似，而不仅仅是当它们碰巧共享术语时。 多维标度（MDS）和神经网络理论是本文的理论基础。 这种方法被证明是类似于目前最好的技术，统计语义分析的文件：潜在语义索引（LSI）。 这项工作提出了一个推广的LSI，线性和度量的技术，非线性和非度量的技术。 这项工作有望提供一个坚实的理论框架，基于MDS的文档索引，推进使用神经网络技术在文档索引，并帮助当前的文档检索方法的定量评估。 ***

9221276 Cottrell Learning Semantic Representations for Information Retrieval This is the first year funding of a three-year continuing award. The objective of this project is to develop methods to automatically represent text-based documents from a large collection in a way which facilitates semantically precise retrieval. A critical problem in representing documents is that words in the documents are not accurate descriptors of document content. This is in part due to the polysemy of natural language: A single concept can be described in many different ways. Most current approaches fail to account for this, as they determine semantic relevance using co-occurrence of words in documents. The approach is to index documents so that they are representationally similar when they are semantically related, not just when they coincidentally share terms. Multidimensional Scaling (MDS) and Neural Network theory are foundations of the work. This approach is demonstrated to be similar to the best current technique for statistical semantic analysis of documents: Latent Semantic Indexing (LSI). The work suggests a generalization of LSI, a linear and metric technique, to non-linear and non-metric techniques. This work is expected to provide a well-founded theoretical framework for document indexing based on MDS, to advance the use of neural network techniques in document indexing, and to help in the quantitative evaluation of current document retrieval methods. ***