text: There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 on the 374 analogy questions, statistically equivalent to the average human score of 57 . On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.
  @cite_12: We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.
summary: The problem of relation extraction is, given an input document and a specific relation @math , extract all pairs of entities (if any) that have the relation @math in the document. The problem was introduced as part of the Message Understanding Conferences (MUC) in 1998. zelenko03 present a kernel method for extracting the relations person-affiliation and organization-location . For example, in the sentence John Smith is the chief scientist of the Hardcom Corporation,'' there is a person-affiliation relation between John Smith'' and Hardcom Corporation'' @cite_12 . This is similar to the problem of classifying semantic relations (), except that information extraction focuses on the relation between a specific pair of entities in a specific document, rather than a general pair of words in general text. Therefore an algorithm for classifying semantic relations should be useful for information extraction.