Vangelis Karkaletsis


2019

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A study of semantic augmentation of word embeddings for extractive summarization
Nikiforos Pittaras | Vangelis Karkaletsis
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources

In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to sentence-level representations produced by aggregated deep neural word embeddings. Multiple dimensionality reduction techniques and combination strategies are examined via feature transformation and clustering methods. An experimental evaluation on the MultiLing 2015 MSS dataset illustrates that semantic information can introduce benefits to the extractive summarization process in terms of F1, ROUGE-1 and ROUGE-2 scores, with LSA-based post-processing introducing the largest improvements.

2016

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CLARIN-EL Web-based Annotation Tool
Ioannis Manousos Katakis | Georgios Petasis | Vangelis Karkaletsis
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents a new Web-based annotation tool, the “CLARIN-EL Web-based Annotation Tool”. Based on an existing annotation infrastructure offered by the “Ellogon” language enginneering platform, this new tool transfers a large part of Ellogon’s features and functionalities to a Web environment, by exploiting the capabilities of cloud computing. This new annotation tool is able to support a wide range of annotation tasks, through user provided annotation schemas in XML. The new annotation tool has already been employed in several annotation tasks, including the anotation of arguments, which is presented as a use case. The CLARIN-EL annotation tool is compared to existing solutions along several dimensions and features. Finally, future work includes the improvement of integration with the CLARIN-EL infrastructure, and the inclusion of features not currently supported, such as the annotation of aligned documents.

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Identifying Argument Components through TextRank
Georgios Petasis | Vangelis Karkaletsis
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

2015

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Argument Extraction from News
Christos Sardianos | Ioannis Manousos Katakis | Georgios Petasis | Vangelis Karkaletsis
Proceedings of the 2nd Workshop on Argumentation Mining

2014

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NOMAD: Linguistic Resources and Tools Aimed at Policy Formulation and Validation
George Kiomourtzis | George Giannakopoulos | Georgios Petasis | Pythagoras Karampiperis | Vangelis Karkaletsis
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The NOMAD project (Policy Formulation and Validation through non Moderated Crowd-sourcing) is a project that supports policy making, by providing rich, actionable information related to how citizens perceive different policies. NOMAD automatically analyzes citizen contributions to the informal web (e.g. forums, social networks, blogs, newsgroups and wikis) using a variety of tools. These tools comprise text retrieval, topic classification, argument detection and sentiment analysis, as well as argument summarization. NOMAD provides decision-makers with a full arsenal of solutions starting from describing a domain and a policy to applying content search and acquisition, categorization and visualization. These solutions work in a collaborative manner in the policy-making arena. NOMAD, thus, embeds editing, analysis and visualization technologies into a concrete framework, applicable in a variety of policy-making and decision support settings In this paper we provide an overview of the linguistic tools and resources of NOMAD.

2012

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Task-Driven Linguistic Analysis based on an Underspecified Features Representation
Stasinos Konstantopoulos | Valia Kordoni | Nicola Cancedda | Vangelis Karkaletsis | Dietrich Klakow | Jean-Michel Renders
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires. The core idea is to generalize feature values into feature value distributions, representing under-specified feature values, and to fit linguistic pipelines with a back-channel of specification requests through which subsequent components can declare to preceding ones the importance of narrowing the value distribution of particular features that are critical for the current task.

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Evaluation of Online Dialogue Policy Learning Techniques
Alexandros Papangelis | Vangelis Karkaletsis | Fillia Makedon
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers. A DS that employs some form of adaptation to the environment and its users is called an Adaptive Dialogue System (ADS). A significant part of the research community has lately focused on ADS and many existing or novel techniques are being applied to this problem. One of the most promising techniques is Reinforcement Learning (RL) and especially online RL. This paper focuses on online RL techniques used to achieve adaptation in Dialogue Management and provides an evaluation of various such methods in an effort to aid the designers of ADS in deciding which method to use. To the best of our knowledge there is no other work to compare online RL techniques on the dialogue management problem.

2010

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United we Stand: Improving Sentiment Analysis by Joining Machine Learning and Rule Based Methods
Vassiliki Rentoumi | Stefanos Petrakis | Manfred Klenner | George A. Vouros | Vangelis Karkaletsis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction.

2009

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Sentiment Analysis of Figurative Language using a Word Sense Disambiguation Approach
Vassiliki Rentoumi | George Giannakopoulos | Vangelis Karkaletsis | George A. Vouros
Proceedings of the International Conference RANLP-2009

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An Intelligent Authoring Environment for Abstract Semantic Representations of Cultural Object Descriptions
Stasinos Konstantopoulos | Vangelis Karkaletsis | Dimitris Bilidas
Proceedings of the EACL 2009 Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education (LaTeCH – SHELT&R 2009)

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Proceedings of the Workshop on Biomedical Information Extraction
Guergana Savova | Vangelis Karkaletsis | Galia Angelova
Proceedings of the Workshop on Biomedical Information Extraction

2008

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BOEMIE Ontology-Based Text Annotation Tool
Pavlina Fragkou | Georgios Petasis | Aris Theodorakos | Vangelis Karkaletsis | Constantine Spyropoulos
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The huge amount of the available information in the Web creates the need of effective information extraction systems that are able to produce metadata that satisfy user’s information needs. The development of such systems, in the majority of cases, depends on the availability of an appropriately annotated corpus in order to learn extraction models. The production of such corpora can be significantly facilitated by annotation tools that are able to annotate, according to a defined ontology, not only named entities but most importantly relations between them. This paper describes the BOEMIE ontology-based annotation tool which is able to locate blocks of text that correspond to specific types of named entities, fill tables corresponding to ontology concepts with those named entities and link the filled tables based on relations defined in the domain ontology. Additionally, it can perform annotation of blocks of text that refer to the same topic. The tool has a user-friendly interface, supports automatic pre-annotation, annotation comparison as well as customization to other annotation schemata. The annotation tool has been used in a large scale annotation task involving 3,000 web pages regarding athletics. It has also been used in another annotation task involving 503 web pages with medical information, in different languages.

2005

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Exploiting OWL Ontologies in the Multilingual Generation of Object Descriptions
Ion Androutsopoulos | Spyros Kallonis | Vangelis Karkaletsis
Proceedings of the Tenth European Workshop on Natural Language Generation (ENLG-05)

2003

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Demonstration of the CROSSMARC System
Vangelis Karkaletsis | Constantine D. Spyropoulos | Dimitris Souflis | Claire Grover | Ben Hachey | Maria Teresa Pazienza | Michele Vindigni | Emmanuel Cartier | Jose Coch
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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Evaluating specifications for controlled Greek
Marina Vassiliou | Stella Markantonatou | Yanis Maistros | Vangelis Karkaletsis
EAMT Workshop: Improving MT through other language technology tools: resources and tools for building MT

2002

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Ellogon: A New Text Engineering Platform
Georgios Petasis | Vangelis Karkaletsis | Georgios Paliouras | Ion Androutsopoulos | Constantine D. Spyropoulos
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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PatEdit: An Information Extraction Pattern Editor for Fast System Customization
Dimitra Farmakiotou | Vangelis Karkaletsis | Ioannis Koutsias | George Petasis | Constantine D. Spyropoulos
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Multilingual XML-Based Named Entity Recognition for E-Retail Domains
Claire Grover | Scott McDonald | Donnla Nic Gearailt | Vangelis Karkaletsis | Dimitra Farmakiotou | Georgios Samaritakis | Georgios Petasis | Maria Teresa Pazienza | Michele Vindigni | Frantz Vichot | Francis Wolinski
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Stacking Classifiers for Anti-Spam Filtering of E-Mail
Georgios Sakkis | Ion Androutsopoulos | Georgios Paliouras | Vangelis Karkaletsis | Constantine D. Spyropoulos | Panagiotis Stamatopoulos
Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing

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Using Machine Learning to Maintain Rule-based Named-Entity Recognition and Classification Systems
Georgios Petasis | Frantz Vichot | Francis Wolinski | Georgios Paliouras | Vangelis Karkaletsis | Constantine D. Spyropoulos
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

1993

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The use of terminological knowledge bases in software localisation
Vangelis Karkaletsis | Constantine D. Spyropoulos | George A. Vouros
Third International EAMT Workshop: Machine Translation and the Lexicon

This paper describes the work that was undertaken in the Glossasoft project in the area of terminology management. Some of the draw-backs of existing terminology management systems are outlined and an alternative approach to maintaining terminological data is proposed. The approach which we advocate relies on knowledge-based representation techniques. These are used to model conceptual knowledge about the terms included in the database, general knowledge about the subject domain, application-specific knowledge, and - of course - language-specific terminological knowledge. We consider the multifunctionality of the proposed architecture to be one of its major advantages. To illustrate this, we outline how the knowledge representation scheme, which we suggest, could be drawn upon in message generation and machine-assisted translation.