Computerlinguistik
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Automatic summarization systems usually are trained and evaluated in a particular domain with fixed data sets. When such a system is to be applied to slightly different input, labor- and cost-intensive annotations have to be created to retrain the system. We deal with this problem by providing users with a GUI which allows them to correct automatically produced imperfect summaries. The corrected summary in turn is added to the pool of training data. The performance of the system is expected to improve as it adapts to the new domain.
In this paper we present work in developing a computerized grammar for the Latin language. It demonstrates the principles and challenges in developing a grammar for a natural language in a modern grammar formalism. The grammar presented here provides a useful resource for natural language processing applications in different fields. It can be easily adopted for language learning and use in language technology for Cultural Heritage like translation applications or to support post-correction of document digitization.
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
(2009)
In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.
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 demo presents a minimalist, off-the-shelf AND tool which provides a fundamental AND operation, the comparison of two publications with ambiguous authors, as an easily accessible HTTP interface. The tool implements this operation using standard AND functionality, but puts particular emphasis on advanced methods from natural language processing (NLP) for comparing publication title semantics.
Lexical resources are often represented in table form, e. g., in relational databases, or represented in specially marked up texts, for example, in document based XML models. This paper describes how it is possible to model lexical structures as graphs and how this model can be used to exploit existing lexical resources and even how different types of lexical resources can be combined.
In this contribution we present some work of the R&D European project “LIRICS” and of the ISO/TC 37/SC 4 committee related to the topic of interoperability and re-use of language resources. We introduce some basic mechanisms of the standardization work in ISO and describe in more details the general approach on how to cope with the annotation of language data within ISO.
The use of digital resources and tools across humanities disciplines is steadily increasing, giving rise to new research paradigms and associated methods that are commonly subsumed under the term digital humanities. Digital humanities does not constitute a new discipline in itself, but rather a new approach to humanities research that cuts across different existing humanities disciplines. While digital humanities extends well beyond language-based research, textual resources and spoken language materials play a central role in most humanities disciplines.
The ISOcat registry reloaded
(2012)
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.