Viktor Pekar


2017

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Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media
Viktor Pekar | Jane Binner
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Consumer spending is an important macroeconomic indicator that is used by policy-makers to judge the health of an economy. In this paper we present a novel method for predicting future consumer spending from social media data. In contrast to previous work that largely relied on sentiment analysis, the proposed method models consumer spending from purchase intentions found on social media. Our experiments with time series analysis models and machine-learning regression models reveal utility of this data for making short-term forecasts of consumer spending: for three- and seven-day horizons, prediction variables derived from social media help to improve forecast accuracy by 11% to 18% for all the three models, in comparison to models that used only autoregressive predictors.

2014

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UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis
Viktor Pekar | Naveed Afzal | Bernd Bohnet
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Exploring Options for Fast Domain Adaptation of Dependency Parsers
Viktor Pekar | Juntao Yu | Mohab El-karef | Bernd Bohnet
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

2009

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Unsupervised Relation Extraction for Automatic Generation of Multiple-Choice Questions
Naveed Afzal | Viktor Pekar
Proceedings of the International Conference RANLP-2009

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Proceedings of the Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning
Iustina Ilisei | Viktor Pekar | Silvia Bernardini
Proceedings of the Workshop on Natural Language Processing Methods and Corpora in Translation, Lexicography, and Language Learning

2008

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Development and Alignment of a Domain-Specific Ontology for Question Answering
Shiyan Ou | Viktor Pekar | Constantin Orasan | Christian Spurk | Matteo Negri
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

With the appearance of Semantic Web technologies, it becomes possible to develop novel, sophisticated question answering systems, where ontologies are usually used as the core knowledge component. In the EU-funded project, QALL-ME, a domain-specific ontology was developed and applied for question answering in the domain of tourism, along with the assistance of two upper ontologies for concept expansion and reasoning. This paper focuses on the development of the QALL-ME ontology in the tourism domain and its alignment with the upper ontologies - WordNet and SUMO. The design of the ontology is presented in the paper, and a semi-automatic alignment procedure is described with some alignment results given as well. Furthermore, the aligned ontology was used to semantically annotate original data obtained from the tourism web sites and natural language questions. The storage schema of the annotated data and the data access method for retrieving answers from the annotated data are also reported in the paper.

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Translation universals: do they exist? A corpus-based NLP study of convergence and simplification
Gloria Corpas Pastor | Ruslan Mitkov | Naveed Afzal | Viktor Pekar
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

Convergence and simplification are two of the so-called universals in translation studies. The first one postulates that translated texts tend to be more similar than non-translated texts. The second one postulates that translated texts are simpler, easier-to-understand than non-translated ones. This paper discusses the results of a project which applies NLP techniques over comparable corpora of translated and non-translated texts in Spanish seeking to establish whether these two universals hold Corpas Pastor (2008).

2006

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Automatic Detection of Orthographics Cues for Cognate Recognition
Andrea Mulloni | Viktor Pekar
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Present-day machine translation technologies crucially depend on the size and quality of lexical resources. Much of recent research in the area has been concerned with methods to build bilingual dictionaries automatically. In this paper we propose a methodology for the automatic detection of cognates between two languages based solely on the orthography of words. From a set of known cognates, the method induces rules capturing regularities of orthographic mutations that a word undergoes when migrating from one language into the other. The rules are then applied as a preprocessing step before measuring the orthographic similarity between putative cognates. As a result, the method allows to achieve an improvement in the F-measure of 11,86% in comparison with detecting cognates based only on the edit distance between them.

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Acquisition of Verb Entailment from Text
Viktor Pekar
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2004

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A Comparison of Summarisation Methods Based on Term Specificity Estimation
Constantin Orăsan | Viktor Pekar | Laura Hasler
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Categorizing Web Pages as a Preprocessing Step for Information Extraction
Viktor Pekar | Richard Evans | Ruslan Mitkov
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Feature Weighting for Co-occurrence-based Classification of Words
Viktor Pekar | Michael Krkoska | Steffen Staab
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Linguistic Preprocessing for Distributional Classification of Words
Viktor Pekar
Proceedings of the Workshop on Enhancing and Using Electronic Dictionaries

2003

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Word classification based on combined measures of distributional and semantic similarity
Viktor Pekar | Steffen Staab
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Taxonomy Learning - Factoring the Structure of a Taxonomy into a Semantic Classification Decision
Viktor Pekar | Steffen Staab
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Specification In Terms Of Interactional Properties As A Way To Optimize Semantic Representation Of Spatial Expressions
Viktor Pekar
Proceedings of the ACL 2001 Workshop on Temporal and Spatial Information Processing