Valence extraction using EM selection and co-occurrence matrices

Valence extraction using EM selection and co-occurrence matrices

This paper discusses two new procedures for extracting verb valences from raw texts, with an application to the Polish language. The first novel technique, the EM selection algorithm, performs unsupervised disambiguation of valence frame forests, obtained by applying a non-probabilistic deep grammar parser and some post-processing to the text. The second new idea concerns filtering of incorrect...

Term Extraction Using Verb Co-Occurrence

The Normalization of Occurrence and Co-occurrence Matrices in Bibliometrics using Cosine Similarities and Ochiai Coefficients

We prove that Ochiai similarity of the co-occurrence matrix is equal to cosine similarity in the underlying occurrence matrix. Neither the cosine nor the Pearson correlation should be used for the normalization of co-occurrence matrices because the similarity is then normalized twice, and therefore over-estimated; the Ochiai coefficient can be used instead. Results are shown using a small matri...

Co-occurrence Matrices for Volumetric Data

In this paper, we investigate a new approach to the cooccurrence matrix currently used to extract textural features: co-occurrence matrices for volumetric data. While traditional texture metrics have concentrated on 2D texture, 3D imaging modalities are becoming more and more prevalent, providing the possibility of examining texture as a volumetric phenomenon. Just as computer graphics have use...

Argument co-occurrence matrix as a description of verb valence

A new description of verb valence is proposed. Rather than by full valence frames, verb subcategorization is described by the list of arguments and the matrix of five distinct pairwise argument interactions. This approach is argued to be computationally robust and sufficient although it has been initially motivated only by the need of a reliable test set for machine verb valence learning. Speak...