Nikos Fakotakis

Also published as: N. Fakotakis, Nikos D. Fakotakis


2010

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Heterogeneous Sensor Database in Support of Human Behaviour Analysis in Unrestricted Environments: The Audio Part
Stavros Ntalampiras | Todor Ganchev | Ilyas Potamitis | Nikos Fakotakis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In the present paper we report on a recent effort that resulted in the establishment of a unique multimodal database, referred to as the PROMETHEUS database. This database was created in support of research and development activities, performed within the European Commission FP7 PROMETHEUS project, aiming at the creation of a framework for monitoring and interpretation of human behaviours in unrestricted indoors and outdoors environments. In the present paper we discuss the design and the implementation of the audio part of the database and offer statistical information about the audio content. Specifically, it contains single-person and multi-person scenarios, but also covers scenarios with interactions between groups of people. The database design was conceived with extended support of research and development activities devoted to detection of typical and atypical events, emergency and crisis situations, which assist for achieving situational awareness and more reliable interpretation of the context in which humans behave. The PROMETHEUS database allows for embracing a wide range of real-world applications, including smart-home and human-robot interaction interfaces, indoors/outdoors public areas surveillance, airport terminals or city park supervision, etc. A major portion of the PROMETHEUS database will be made publically available by the end of year 2010.

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Vergina: A Modern Greek Speech Database for Speech Synthesis
Alexandros Lazaridis | Theodoros Kostoulas | Todor Ganchev | Iosif Mporas | Nikos Fakotakis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The present paper outlines the Vergina speech database, which was developed in support of research and development of corpus-based unit selection and statistical parametric speech synthesis systems for Modern Greek language. In the following, we describe the design, development and implementation of the recording campaign, as well as the annotation of the database. Specifically, a text corpus of approximately 5 million words, collected from newspaper articles, periodicals, and paragraphs of literature, was processed in order to select the utterances-sentences needed for producing the speech database and to achieve a reasonable phonetic coverage. The broad coverage and contents of the selected utterances-sentences of the database ― text corpus collected from different domains and writing styles ― makes this database appropriate for various application domains. The database, recorded in audio studio, consists of approximately 3,000 phonetically balanced Modern Greek utterances corresponding to approximately four hours of speech. Annotation of the Vergina speech database was performed using task-specific tools, which are based on a hidden Markov model (HMM) segmentation method, and then manual inspection and corrections were performed.

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The PlayMancer Database: A Multimodal Affect Database in Support of Research and Development Activities in Serious Game Environment
Theodoros Kostoulas | Otilia Kocsis | Todor Ganchev | Fernando Fernández-Aranda | Juan J. Santamaría | Susana Jiménez-Murcia | Maher Ben Moussa | Nadia Magnenat-Thalmann | Nikos Fakotakis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The present paper reports on a recent effort that resulted in the establishment of a unique multimodal affect database, referred to as the PlayMancer database. This database was created in support of the research and development activities, taking place within the PlayMancer project, which aim at the development of a serious game environment in support of treatment of patients with behavioural and addictive disorders, such as eating disorders and gambling addictions. Specifically, for the purpose of data collection, we designed and implemented a pilot trial with healthy test subjects. Speech, video and bio-signals (pulse-rate, SpO2) were captured synchronously, during the interaction of healthy people with a number of video games. The collected data were annotated by the test subjects (self-annotation), targeting proper interpretation of the underlying affective states. The broad-shouldered design of the PlayMancer database allows its use for the needs of research on multimodal affect-emotion recognition and multimodal human-computer interaction in serious games environment.

2008

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Eksairesis: A Domain-Adaptable System for Ontology Building from Unstructured Text
Katia Lida Kermanidis | Aristomenis Thanopoulos | Manolis Maragoudakis | Nikos Fakotakis
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes Eksairesis, a system for learning economic domain knowledge automatically from Modern Greek text. The knowledge is in the form of economic terms and the semantic relations that govern them. The entire process in based on the use of minimal language-dependent tools, no external linguistic resources, and merely free, unstructured text. The methodology is thereby easily portable to other domains and other languages. The text is pre-processed with basic morphological annotation, and semantic (named and other) entities are identified using supervised learning techniques. Statistical filtering, i.e. corpora comparison is used to extract domain terms and supervised learning is again employed to detect the semantic relations between pairs of terms. Advanced classification schemata, ensemble learning, and one-sided sampling, are experimented with in order to deal with the noise in the data, which is unavoidable due to the low pre-processing level and the lack of sophisticated resources. An average 68.5% f-score over all the classes is achieved when learning semantic relations. Bearing in mind the use of minimal resources and the highly automated nature of the process, classification performance is very promising, compared to results reported in previous work.

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A Real-World Emotional Speech Corpus for Modern Greek
Theodoros Kostoulas | Todor Ganchev | Iosif Mporas | Nikos Fakotakis
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The present paper deals with the design and the annotation of a Greek real-world emotional speech corpus. The speech data consist of recordings collected during the interaction of naïve users with a smart-home dialogue system. Annotation of the speech data with respect to the uttered command and emotional state was performed. Initial experimentations towards recognizing negative emotional states were performed and the experimental results indicate the range of difficulties when dealing with real-world data.

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The MoveOn Motorcycle Speech Corpus
Thomas Winkler | Theodoros Kostoulas | Richard Adderley | Christian Bonkowski | Todor Ganchev | Joachim Köhler | Nikos Fakotakis
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

A speech and noise corpus dealing with the extreme conditions of the motorcycle environment is developed within the MoveOn project. Speech utterances in British English are recorded and processed approaching the issue of command and control and template driven dialog systems on the motorcycle. The major part of the corpus comprises noisy speech and environmental noise recorded on a motorcycle, but several clean speech recordings in a silent environment are also available. The corpus development focuses on distortion free recordings and accurate descriptions of both recorded speech and noise. Not only speech segments are annotated but also annotation of environmental noise is performed. The corpus is a small-sized speech corpus with about 12 hours of clean and noisy speech utterances and about 30 hours of segments with environmental noise without speech. This paper addresses the motivation and development of the speech corpus and finally presents some statistics and results of the database creation.

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Audio Database in Support of Potentiel Threat and Crisis Situation Management
Stavros Ntalampiras | Ilyas Potamitis | Todor Ganchev | Nikos Fakotakis
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes a corpus consisting of audio data for automatic space monitoring based solely on the perceived acoustic information. The particular database is created as part of a project aiming at the detection of abnormal events, which lead to life-threatening situations or property damage. The audio corpus is composed of vocal reactions and environmental sounds that are usually encountered in atypical situations. The audio data is composed of three parts: Phase I - professional sound effects collections, Phase II recordings obtained from action and drama movies and Phase III - vocal reactions related to real-world emergency events as retrieved from television, radio broadcast news, documentaries etc. The annotation methodology is given in details along with preliminary classification results and statistical analysis of the dataset regarding Phase I. The main objective of such a dataset is to provide training data for automatic recognition machines that detect hazardous situations and to provide security enhancement in public environments, which otherwise require human supervision.

2006

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Dealing with Imbalanced Data using Bayesian Techniques
Manolis Maragoudakis | Katia Kermanidis | Aristogiannis Garbis | Nikos Fakotakis
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

For the present work, we deal with the significant problem of high imbalance in data in binary or multi-class classification problems. We study two different linguistic applications. The former determines whether a syntactic construction (environment) co-occurs with a verb in a natural text corpus consists a subcategorization frame of the verb or not. The latter is called Name Entity Recognition (NER) and it concerns determining whether a noun belongs to a specific Name Entity class. Regarding the subcategorization domain, each environment is encoded as a vector of heterogeneous attributes, where a very high imbalance between positive and negative examples is observed (an imbalance ratio of approximately 1:80). In the NER application, the imbalance between a name entity class and the negative class is even greater (1:120). In order to confront the plethora of negative instances, we suggest a search tactic during training phase that employs Tomek links for reducing unnecessary negative examples from the training set. Regarding the classification mechanism, we argue that Bayesian networks are well suited and we propose a novel network structure which efficiently handles heterogeneous attributes without discretization and is more classification-oriented. Comparing the experimental results with those of other known machine learning algorithms, our methodology performs significantly better in detecting examples of the rare class.

2004

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Learning Greek Verb Complements: Addressing the Class Imbalance
Katia Kermanidis | Manolis Maragoudakis | Nikos Fakotakis | George Kokkinakis
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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INSPIRE: Evaluation of a Smart-Home System for Infotainment Management and Device Control
Sebastian Möller | Jan Krebber | Alexander Raake | Paula Smeele | Martin Rajman | Mirek Melichar | Vincenzo Pallotta | Gianna Tsakou | Basilis Kladis | Anestis Vovos | Jettie Hoonhout | Dietmar Schuchardt | Nikos Fakotakis | Todor Ganchev | Ilyas Potamitis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Corpus Design, Recording and Phonetic Analysis of Greek Emotional Database
Nikos Fakotakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A Bayesian Model for Shallow Syntactic Parsing of Natural Language Texts
Manolis Maragoudakis | Nikos Fakotakis | George Kokkinakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

For the present work, we introduce and evaluate a novel Bayesian syntactic shallow parser that is able to perform robust detection of pairs of subject-object and subject-direct object-indirect object for a given verb, in a natural language sentence. The shallow parser infers on the correct subject-object pairs based on knowledge provided by Bayesian network learning from annotated text corpora. The DELOS corpus, a collection of economic domain texts that has been automatically annotated using various morphological and syntactic tools was used as training material. Our shallow parser makes use of limited linguistic input. More specifically, we consider only part of speech tagging, the voice and the mood of the verb as well as the head word of a noun phrase. For the task of detecting the head word of a phrase we used a sentence boundary detector. Identifying the head word of a noun phrase, i.e. the word that holds the morphological information (case, number) of the whole phrase, also proves to be very helpful for our task as its morphological tag is all the information that is needed regarding the phrase. The evaluation of the proposed method was performed against three other machine learning techniques, namely naive Bayes, k-Nearest Neighbor and Support Vector Machines, methods that have been previously applied to natural language processing tasks with satisfactory results. The experimental outcomes portray a satisfactory performance of our proposed shallow parser, which reaches almost 92 per cent in terms of precision.

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Learning to Predict Pitch Accents Using Bayesian Belief Networks for Greek Language
Panagiotis Zervas | Manolis Maragoudakis | Nikos Fakotakis | George Kokkinakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Cypriot Speech Database: Data Collection and Greek to Cypriot Dialect Adaptation
Nikos Fakotakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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OrienTel - Telephony Databases Across Northern Africa and the Middle East
Dorota Iskra | Rainer Siemund | Jamal Borno | Asuncion Moreno | Ossama Emam | Khalid Choukri | Oren Gedge | Herbert Tropf | Albino Nogueiras | Imed Zitouni | Anastasios Tsopanoglou | Nikos Fakotakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Bayesian Semantics Incorporation to Web Content for Natural Language Information Retrieval
Manolis Maragoudakis | Nikos Fakotakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A graphical Tool for Handling Rule Grammars in Java Speech Grammar Format
Kallirroi Georgila | Nikos Fakotakis | George Kokkinakis
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

2003

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Text Tokenization for Knowledge-free Automatic Extraction of Lexical Similarities
Aristomenis Thanopoulos | Nikos Fakotakis | George Kokkinakis
Actes de la 10ème conférence sur le Traitement Automatique des Langues Naturelles. Posters

Previous studies on automatic extraction of lexical similarities have considered as semantic unit of text the word. However, the theory of contextual lexical semantics implies that larger segments of text, namely non-compositional multiwords, are more appropriate for this role. We experimentally tested the applicability of this notion applying automatic collocation extraction to identify and merge such multiwords prior to the similarity estimation process. Employing an automatic WordNet-based comparative evaluation scheme along with a manual evaluation procedure, we ascertain improvement of the extracted similarity relations.

2002

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Combining Bayesian and Support Vector Machines Learning to automatically complete Syntactical Information for HPSG-like Formalisms
Manolis Maragoudakis | Katia Kermanidis | Nikos Fakotakis | George Kokkinakis
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Comparative Evaluation of Collocation Extraction Metrics
Aristomenis Thanopoulos | Nikos Fakotakis | George Kokkinakis
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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DELOS: An Automatically Tagged Economic Corpus for Modern Greek
Katia Lida Kermanidis | Nikos Fakotakis | George Kokkinakis
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Incremental Construction of Compact Acyclic NFAs
Kyriakos N. Sgarbas | Nikos D. Fakotakis | George K. Kokkinakis
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

2000

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Automatic Text Categorization In Terms Of Genre and Author
Efstathios Stamatatos | Nikos Fakotakis | George Kokkinakis
Computational Linguistics, Volume 26, Number 4, December 2000

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GRUHD: A Greek database of Unconstrained Handwriting
E. Kavallieratou | N. Liolios | E. Koutsogeorgos | N. Fakotakis | G. Kokkinakis
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Automatic Extraction of Semantic Similarity of Words from Raw Technical Texts
Aristomenis Thanopoulos | Nikos Fakotakis | George Kokkinakis
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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A Graphical Parametric Language-Independent Tool for the Annotation of Speech Corpora
Kallirroi Georgila | Nikos Fakotakis | George Kokkinakis
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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Text Genre Detection Using Common Word Frequencies
E. Stamatatos | N. Fakotakis | G. Kokkinakis
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Automatic Extraction of Semantic Relations from Specialized Corpora
Aristomenis Thanopoulos | Nikos Fakotakis | George Kokkinakis
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

1999

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Automatic Authorship Attribution
E. Stamatatos | N. Fakotakis | G. Kokkinakis
Ninth Conference of the European Chapter of the Association for Computational Linguistics