Alan Akbik

Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling

Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data. Experimental results show that our method outperforms existing methods. We use our method to generate Proposition Banks with high to reasonable quality for 7 languages in three language families and release these resources to the research community.

SCHNÄPPER: A Web Toolkit for Exploratory Relation Extraction

We present SCHN ¨ APPER, a web toolkit for Exploratory Relation Extraction (ERE). The tool allows users to identify relations of interest in a very large text corpus in an exploratory and highly interactive fashion. With this tool, we demonstrate the easeof-use and intuitive nature of ERE, as well as its applicability to large corpora. We show how users can formulate exploratory, natural language-like pattern queries that return relation instances. We also show how automatically computed suggestions are used to guide the exploration process. Finally, we demonstrate how users create extractors with SCHN ¨ APPER once a relation of interest is identified.

Extracting a Repository of Events and Event References from News Clusters

In this paper, we prose to build a repository of events and event references from clusters of news articles. We present an automated approach that is based on the hypothesis that if two sentences are a) found in the same cluster of news articles and b) contain temporal expressions that reference the same point in time, they are likely to refer to the same event. This allows us to group similar sentences together and apply open-domain Information Extraction (OpenIE) methods to extract lists of textual references for each detected event. We outline our proposed approach and present a preliminary evaluation in which we extract events and references from 20 clusters of online news. Our experiments indicate that for the largest part our hypothesis holds true, pointing to a strong potential for applying our approach to building an event repository. We illustrate cases in which our hypothesis fails and discuss ways for addressing sources or errors.