(1997) find semantic correspondences in parallel (different language) corpora through latent semantic analysis (LSA).

We then use LSA — previously shown by (Dumais et al., 1997) and (Gliozzo and Strapparava, 2005) to be useful for this task —to induce the latent semantic dimensions of documents and words respectively, hypothesizing that word etymological ancestors will lead to semantic dimensions that transcend language boundaries.

3.4 Cross-lingual text categorization in a latent semantic space adding etymology

The clue to why the increase when using LSA is lower than for English trainingfltalian testing is in the way LSA operates — it relies heavily on word co-occurrences in finding the latent semantic dimensions of documents and words.

Thus, each latent semantic class corresponds to one of the semantic tags found in labeled data.

(I) Semantic Tags (Si): Each word 21),- of a given utterance with Nj words, uj={wi}§:j1€U, j=1,..|U |, from a set of utterances U, is associated with a latent semantic tag (state) variable 3168, where 8 is the set of semantic tags.

latent semantic tag

We evaluate two approaches employing LDA and probabilistic latent semantic analysis (PLSA) distributions respectively.

Probabilistic latent semantic analysis (PLSA) (Hofman-n, 1999) is a typical instance and used widely.

PLSA is the probabilistic variant of latent semantic analysis (LSA) (Choi et al., 2001), and offers a more solid statistical foundation.

In this paper, we propose to combine (i) term generalization approaches such as word clustering and latent semantic analysis (LSA) and (ii) structured kernels to improve the adaptability of relation extractors to new text genres/domains.

We study two ways for term generalization in tree kernels: Brown words clusters and Latent Semantic Analysis (LSA), both briefly described next.

The latter is derived in two ways with: (a) Brown word clustering (Brown et al., 1992); and (b) Latent Semantic Analysis (LSA).

Top: the dependency tree of the sentence is annotated with latent semantic states by GUSP.

GUSP produces a semantic parse of the question by annotating its dependency tree with latent semantic states.

GUSP starts with the dependency tree of a sentence and produces a semantic parse by annotating the nodes and edges with latent semantic states derived from the database.