Extraction of V-N-Collocations from Text Corpora: A Feasibility Study for German

TL;DR

This study evaluates a statistical method for extracting verb-noun collocations from German texts, addressing language-specific challenges and optimizing precision and recall for different linguistic and computational goals.

Contribution

It adapts and assesses a statistical approach for German V-N collocation extraction, highlighting modifications to improve accuracy and discussing trade-offs between precision and recall.

Findings

Achieved 97.8% precision with large corpora using restrictive methods.

Less restrictive methods yield higher recall but lower precision.

Large corpora can compensate for lower recall in practical applications.

Abstract

The usefulness of a statistical approach suggested by Church et al. (1991) is evaluated for the extraction of verb-noun (V-N) collocations from German text corpora. Some problematic issues of that method arising from properties of the German language are discussed and various modifications of the method are considered that might improve extraction results for German. The precision and recall of all variant methods is evaluated for V-N collocations containing support verbs, and the consequences for further work on the extraction of collocations from German corpora are discussed. With a sufficiently large corpus (>= 6 mio. word-tokens), the average error rate of wrong extractions can be reduced to 2.2% (97.8% precision) with the most restrictive method, however with a loss in data of almost 50% compared to a less restrictive method with still 87.6% precision. Depending on the goal to be achieved, emphasis can be put on a high recall for lexicographic purposes or on high precision for automatic lexical acquisition, in each case unfortunately leading to a decrease of the corresponding other variable. Low recall can still be acceptable if very large corpora (i.e. 50 - 100 million words) are available or if corpora for special domains are used in addition to the data found in machine readable (collocation) dictionaries.