Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Emotional Processes of Foreign Language Learning Situated in Real-Time Teacher Support

The dynamic turn in the field of psychology of foreign-language learning has inspired researchers to capture the nitty gritty dynamics of development in inter- or intra-individual variables. Despite the growing number of techniques for analyzing dynamics, there is still a need for techniques that capture how intra-individual dynamics are situated in a changing context. One of the techniques that can contribute to this knowledge is a clustering technique called Kohonen’s Self-Organizing Maps (SOM). In this study, we aimed to explore the intra-individual process of foreign-language enjoyment and foreign-language classroom anxiety in alignment with teachers’ level of emotional support during teacher-student interactions for two dyads. The findings indicated the emergence of recurring patterns of teacher support, and student anxiety and enjoyment. These patterns highlight the self-organizing nature of these teacher-student interactions, the bi-direction nature of this process, and in general, the notion of teachers and students as dynamic systems. Moreover, the specific nature of the emergent patterns suggests that the traditional positive association between teacher support and student affect may (mostly, but not always) be generalized to real-time and real-life processes. And finally, our results point toward the importance of the student, in determining the affective nature of the interactions from moment to moment

Self-Organized Ordering of Terms and Documents in NSF Awards Data

We present the results of an analysis of a text corpus of 129,000 abstracts of NSF-sponsored basic research projects between years 1990 and 2003. The methods used in the analysis include term extraction based on a reference corpus and an entropy measure, and the Self-Organizing Map algorithm for the formation of a term map and a document map. Methodologically, the basic approach is based on earlier developments, such as word category maps and the WEBSOM method, but in the level of details, we report several new aspects and quantitative comparison results between methodological variants in this article. The data covers a quite large proportion of US-based scientific research during recent years. The analysis results indicate the basic patterns discernable in the data, both at the level of the awards and at the terminology used in them

Building a Finnish SOM-based ontology concept tagger and harvester

Kehitän luonnollisessa kielessä ilmenevien sanojen merkitysten eroteluun sopivaa automaatista koneoppivaa työkalua. Laskennallinen malli perustuu itseoppivaan kartaan (SOM, Self-Organizing Map) ja annetuun suomenkieliseen semantisen webin ontologiaan. Malli oppii tunnistamaan käsiteiden ilmenemistä mallitekstistä, johon on annotoitu (tagatu) malliksi aiemmin laaditun ongologian käsiteitä. Koe liityy aiemmin englanninkielisten käsiteiden taggaamiseen liityvään OntoR-koejärjestelyyn joka tutki tekstisyöteessä ilmenevien termien liitämistä SOM-kartan soluihin malliksi annetun annotoidun tekstiesimerkin avulla. Tällainen malli oppii annetun käsitemallin huomatavan niukalla esimerkkiaineistolla ja sopii käytökohteisiin joissa ei ole tarjolla riitävän suurta datamäärää syvän oppimisen neuroverkkomallin opetamiseksi. Suomenkielisen kokeen morfologisen analyysin pohjalla on OMORFI- ja HFST-työkalut. Koneoppimisen toteutava SOM-karta lasketaan SOM-PAK-ohjelmistopaketin avulla. Kehitetyä laskennallista mallia käytetään käsiteiden tunnistamisen lisäksi myös uusien ontologiakäsiteiden ehdokkaiden löytämiseksi