Open Access

We present a supervised machine learning AND system which tackles semantic similarity between publication titles by means of word embeddings. Word embeddings are integrated as external components, which keeps the model small and efficient, while allowing for easy extensibility and domain adaptation. Initial experiments show that word embeddings can improve the Recall and F score of the binary classification sub-task of AND. Results for the clustering sub-task are less clear, but also promising and overall show the feasibility of the approach.

The linguistics community is building a metadata-based infrastructure for the description of its research data and tools. At its core is the ISOcat registry, a collaborative platform to hold a (to be standardized) set of data categories (i.e., field descriptors). Descriptors have definitions in natural language and little explicit interrelations. With the registry growing to many hundred entries, authored by many, it is becoming increasingly apparent that the rather informal definitions and their glossary-like design make it hard for users to grasp, exploit and manage the registry’s content. In this paper, we take a large subset of the ISOcat term set and reconstruct from it a tree structure following the footsteps of schema.org. Our ontological re-engineering yields a representation that gives users a hierarchical view of linguistic, metadata-related terminology. The new representation adds to the precision of all definitions by making explicit information which is only implicitly given in the ISOcat registry. It also helps uncovering and addressing potential inconsistencies in term definitions as well as gaps and redundancies in the overall ISOcat term set. The new representation can serve as a complement to the existing ISOcat model, providing additional support for authors and users in browsing, (re-)using, maintaining, and further extending the community’s terminological metadata repertoire.

Question Answering Systems for retrieving information from Knowledge Graphs (KG) have become a major area of interest in recent years. Current systems search for words and entities but cannot search for grammatical phenomena. The purpose of this paper is to present our research on developing a QA System that answers natural language questions about German grammar. Our goal is to build a KG which contains facts and rules about German grammar, and is also able to answer specific questions about a concrete grammatical issue. An overview of the current research in the topic of QA systems and ontology design is given and we show how we plan to construct the KG by integrating the data in the grammatical information system Grammis, hosted by the Leibniz-Institut für Deutsche Sprache (IDS). In this paper, we describe the construction of the initial KG, sketch our resulting graph, and demonstrate the effectiveness of such an approach. A grammar correction component will be part of a later stage. The paper concludes with the potential areas for future research.

In this paper, we examine methods to extract different domain-specific relations from the food domain. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. As we need to process a large amount of unlabeled data our methods only require a low level of linguistic processing. This has also the advantage that these methods can provide responses in real time.

This paper deals with multiword lexemes (MWLs), focussing on two types of verbal MWLs: verbal idioms and support verb constructions. We discuss the characteristic properties of MWLs, namely nonstandard compositionality, restricted substitutability of components, and restricted morpho-syntactic flexibility, and we show how these properties may cause serious problems during the analysis, generation, and transfer steps of machine translation systems. In order to cope with these problems, MT lexicons need to provide detailed descriptions of MWL properties. We list the types of information which we consider the necessary minimum for a successful processing of MWLs, and report on some feasibility studies aimed at the automatic extraction of German verbal multiword lexemes from text corpora and machine-readable dictionaries.