Combining Association Measures for Collocation Extraction

Evolving New Lexical Association Measures Using Genetic Programming

Automatic extraction of collocations from large corpora has been the focus of many research efforts. Most approaches concentrate on improving and combining known lexical association measures. In this paper, we describe a genetic programming approach for evolving new association measures, which is not limited to any specific language, corpus, or type of collocation. Our preliminary experimental ...

Collocation Extraction beyond the Independence Assumption

In this paper we start to explore two-part collocation extraction association measures that do not estimate expected probabilities on the basis of the independence assumption. We propose two new measures based upon the well-known measures of mutual information and pointwise mutual information. Expected probabilities are derived from automatically trained Aggregate Markov Models. On three colloc...

Lexical Association Measures - Collocation Extraction

Normalized (Pointwise) Mutual Information in Collocation Extraction

In this paper, we discuss the related information theoretical association measures of mutual information and pointwise mutual information, in the context of collocation extraction. We introduce normalized variants of these measures in order to make them more easily interpretable and at the same time less sensitive to occurrence frequency. We also provide a small empirical study to give more ins...

Multi-label Classification of Semantic Relations in German Nominal Compounds using SVMs

The current study compares lexical association measures for automatic extraction of Estonian particle verbs from the text corpus. The central focus lies on the impact of the corpus size on the performance of the compared symmetrical association measures. Additionally a piece of empirical evidence of the advantage of asymmetric association measure ΔP for the task of collocation extra...