Identification of Comparable Argument-Head Relations in Parallel Corpora

We present the machine learning framework that we are developing, in order to support explorative search for non-trivial linguistic configurations in low-density languages (languages with no or few NLP tools). The approach exploits advanced existing analysis tools for high-density languages and word-aligned multi-parallel corpora to bridge across languages. The goal is to find a methodology that minimizes the amount of human expert intervention needed, while producing high-quality search and annotation tools. One of the main challenges is the susceptibility of a complex system combining various automatic analysis components to hard-tocontrol noise from a number of sources. In this paper, we present a series of systematic experiments investigating to what degree the noise issue can be overcome by (i) exploiting more than one perspective on the target language data by considering multiple translations in the parallel corpus, and (ii) using minimally supervised learning techniques such a...

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Kathrin Spreyer, Jonas Kuhn, Bettina Schrader
• Keywords: Education, LREC 2008, Machine Learning, Machine Learning Framework, Non-trivial Linguistic Configurations