I forget to mention that I’m attending ICANN this week, so most will be legacy posts –straight from the conference.

The ResearchChannel is a research consortium dedicated to serve as an online channel for the dissemination of cutting edge technologies. If you want to learn the real stuff under the hood of search engines, just do it through the ResearchChannel. Want to learn the difference between LSA(LSI) and LRA (Latent Relational Analysis)?

Watch the video Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

The abstract states:

“This talk introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. 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; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of 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 (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations 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 noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus.”

Compare this with the many misperceptions SEOs spread about LSI. They  really don’t know what is LSI, nor they have a clue. Still they insist in calling “LSI” something that does not use SVD at all. Duh!

This post is based on a legacy post, originally published in 1/16/2007.

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