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The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years. The main conference papers deal with this topic from different points of view, involving flat as well as deep representations, automatic methods targeting annotation and hybrid symbolic and statistical processing, as well as new Machine Learning-based approaches, but also the creation of language resources for both machines and humans, and methods for testing the latter to optimize their human-machine interaction properties. In line with the general topic, KONVENS-2014 focuses on areas of research which involve this cooperation of information science and computational linguistics: for example learning-based approaches, (cross-lingual) Information Retrieval, Sentiment Analysis, paraphrasing or dictionary and corpus creation, management and usability.
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years.
Current Natural Language Processing (NLP) systems feature high-complexity processing pipelines that require the use of components at different levels of linguistic and application specific processing. These components often have to interface with external e.g. machine learning and information retrieval libraries as well as tools for human annotation and visualization. At the UKP Lab, we are working on the Darmstadt Knowledge Processing Software Repository (DKPro) (Gurevych et al., 2007a; Müller et al., 2008) to create a highly flexible, scalable and easy-to-use toolkit that allows rapid creation of complex NLP pipelines for semantic information processing on demand. The DKPro repository consists of several main parts created to serve the purposes of different NLP application areas
In this paper, we present a suite of flexible UIMA-based components for information retrieval research which have been successfully used (and re-used) in several projects in different application domains. Implementing the whole system as UIMA components is beneficial for configuration management, component reuse, implementation costs, analysis and visualization.
Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society
(2020)
This paper proposes, implements and evaluates a novel, corpus-based approach for identifying categories indicative of the impact of research via a deductive (top-down, from theory to data) and an inductive (bottom-up, from data to theory) approach. The resulting categorization schemes differ in substance. Research outcomes are typically assessed by using bibliometric methods, such as citation counts and patterns, or alternative metrics, such as references to research in the media. Shortcomings with these methods are their inability to identify impact of research beyond academia (bibliometrics) and considering text-based impact indicators beyond those that capture attention (altmetrics). We address these limitations by leveraging a mixed-methods approach for eliciting impact categories from experts, project personnel (deductive) and texts (inductive). Using these categories, we label a corpus of project reports per category schema, and apply supervised machine learning to infer these categories from project reports. The classification results show that we can predict deductively and inductively derived impact categories with 76.39% and 78.81% accuracy (F1-score), respectively. Our approach can complement solutions from bibliometrics and scientometrics for assessing the impact of research and studying the scope and types of advancements transferred from academia to society.
Contents:
1. Johannes Graën, Tannon Kew, Anastassia Shaitarova and Martin Volk, "Modelling Large Parallel Corpora", S. 1-8
2. Pedro Javier Ortiz Suárez, Benoît Sagot and Laurent Romary, "Asynchronous Pipelines for Processing Huge Corpora on Medium to Low Resource Infrastructures", S. 9-16
3. Vladimír Benko, "Deduplication in Large Web Corpora", S. 17-22
4. Mark Davies, "The best of both worlds: Multi-billion word “dynamic” corpora", S. 23-28
5. Adrien Barbaresi, "On the need for domain-focused web corpora", S. 29-32
6. Marc Kupietz, Eliza Margaretha, Nils Diewald, Harald Lüngen and Peter Fankhauser, "What's New in EuReCo? Interoperability, Comparable Corpora, Licensing", S. 33-39
In recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective and subjective text, i.e. subjectivity detection, and the distinction between positive and negative subjective text, i.e. polarity classification. So far, there has been little work examining the distinction between definite polar subjectivity and indefinite polar subjectivity. While the former are utterances which can be categorized as either positive or negative, the latter cannot be categorized as either of these two categories. This paper presents a small set of domain independent features to detect indefinite polar sentences. The features reflect the linguistic structure underlying these types of utterances. We give evidence for the effectiveness of these features by incorporating them into an unsupervised rule-based classifier for sentence-level analysis and compare its performance with supervised machine learning classifiers, i.e. Support Vector Machines (SVMs) and Nearest Neighbor Classifier (kNN). The data used for the experiments are web-reviews collected from three different domains.
Mit den hier zusammengefassten Überlegungen wird ein Problem aufgegriffen, das sich in der germanistisch-philologischen Fachdiskussion durchaus stellt, nämlich, ob es einen Unterschied zwischen Namen und Wörtern gibt. Dieser spiegelt sich etwa im Unterschied zwischen Namenbüchern einerseits und Wörterbüchern andererseits wieder. Hier wird weiter von der auf lexikalische beziehungsweise onomastische Eigenschaften konzentrierten Behandlung von Wörtern oder Namen jeweils auch eine eher enzyklopädische Darstellung der durch die Wörter oder Namen bezeichneten Phänomene unterschieden.
The paper describes preliminary studies regarding the usage of Example-Based Querying for specialist corpora. We outline an infrastructure for its application within the linguistic domain. Example-Based Querying deals with retrieval situations where users would like to explore large collections of specialist texts semantically, but are unable to explicitly name the linguistic phenomenon they look for. As a way out, the proposed framework allows them to input prototypical everyday language examples or cases of doubt, which are automatically processed by CRF and linked to appropriate linguistic texts in the corpus.
ln diesem Beitrag sollen anhand von Materialien aus Gesprächskorpora des IDS Schwierigkeiten und Möglichkeiten der maschinellen Recherche vorgeführt werden. Grundlage dafür sind Gesprächstranskripte, die in digitaler Form vorliegen und in einem System mit Rechercheprozeduren zugreifbar sind. Mit diesem Ziel wird auf Rechercheverfahren zurückgegriffen, die in den 1990er Jahren in einem Projekt SHRGF.S im IDS als Anwendung der COSMAS-Technologie auf Gesprächskorpora entwickelt wurden. Die hier gegebenen Recherchemöglichkeiten werden an einem Auswahlkorpus von Gesprächstranskripten mit einem Gesamtumfang von 87.629 laufenden Wörtern versuchsweise angewendet und in ihren Beschränkungen und ihrer Fruchtbarkeit für explorative Untersuchungen betrachtet. Damit soll ein Beitrag zur Klärung der Frage geleistet werden, welche Recherchemöglichkeiten aus einer gesprächsanalytischen Perspektive vorstellbar und erwünscht sind und insofern bei der weiteren korpustechnologischen Entwicklung berücksichtigt werden sollten.