Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations

Authorship attribution in portuguese using character N-grams

For the Authorship Attribution (AA) task, character n-grams are considered among the best predictive features. In the English language, it has also been shown that some types of character n-grams perform better than others. This paper tackles the AA task in Portuguese by examining the performance of different types of character n-grams, and various combinations of them. The paper also experiments with different feature representations and machine-learning algorithms. Moreover, the paper demonstrates that the performance of the character n-gram approach can be improved by fine-tuning the feature set and by appropriately selecting the length and type of character n-grams. This relatively simple and language-independent approach to the AA task outperforms both a bag-of-words baseline and other approaches, using the same corpus.Mexican Government (Conacyt) [240844, 20161958]; Mexican Government (SIP-IPN) [20171813, 20171344, 20172008]; Mexican Government (SNI); Mexican Government (COFAA-IPN)

Osittain automatisoitujen menetelmien käyttö suorien anglismien tunnistamiseen suomenkielisissä korpusaineistoissa

The goal of this thesis is to investigate methods that could help with harvesting neologisms and more specifically anglicisms (i.e. English-sourced borrowings) in Finnish language. The work is partially motivated by the Global Anglicism Database project to gather anglicisms from various languages, which can serve both as an anglicism dictionary and researchers as a source of information for studying language contact and borrowing either in depth for a specific language or cross-linguistically. A systematic way of harvesting anglicisms in current Finnish language from a suitable corpus is devised. The research examines what kinds of data sources suitable for this goal are available, and what would be the criteria for a useful data source; how to use a data source like that to prepare a good list of anglicisms candidates so that there would be as little irrelevant material as possible but so that no anglicisms would not be lost in the process, and how could the candidates be scored so that the more probable anglicisms would appear closer to the top of a candidate list. Several of Language Bank's Finnish language monolingual corpora are considered. The most important criteria are identified to be the size and genre of the corpus and its annotation. The criteria are explored from the description of corpora on Language Bank's website and available literature and by hands-on examination of the data. Other important measures of corpus suitability are the amount of unannotated foreign language material, amount of noise, and potential anglicism proportion in the corpora. This information is gained via meticulous exploration of random samples of the corpora neologism candidate lists and evaluation on previously gained anglicism set. A combination of two corpora with good coverage of known anglicisms and relatively low amount of noise is chosen as the dataset for the next phase of the anglicism identification process. Anglicism candidate lists are prepared by a process of removing tokens irrelevant for anglicism harvesting. That includes an identifiable part of foreign language material in the corpus, formally recognizable noise, known lemmas of the words that were present in Finnish language around the time just before the major influx of English borrowings to Finnish language started, and their inflected forms. Several methods of scoring candidates are devised that would assign better scores to tokens with higher probability to be an anglicism. The score is based on tokens' frequency in the corpus and relative frequency of the character-level n-grams made out of tokens in representative purely English and purely Finnish corpora. The tokens in the candidate list are scored and ordered, and the resulting list is evaluated based on the ranking of a set of previously identified anglicisms. The method is proved to be somewhat effective; the resulting average ranking of known anglicisms is better than it would be in a randomly sorted candidate list

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)

Topic extraction for ontology learning

This chapter addresses the issue of topic extraction from text corpora for ontology learning. The first part provides an overview of some of the most significant solutions present today in the literature. These solutions deal mainly with the inferior layers of the Ontology Learning Layer Cake. They are related to the challenges of the Terms and Synonyms layers. The second part shows how these pieces can be bound together into an integrated system for extracting meaningful topics. While the extracted topics are not proper concepts as yet, they constitute a convincing approach towards concept building and therefore ontology learning. This chapter concludes by discussing the research undertaken for filling the gap between topics and concepts as well as perspectives that emerge today in the area of topic extraction. © 2011, IGI Global