Oskar Kohonen

Allomorfessor: towards unsupervised morpheme analysis

We extend the unsupervised morpheme segmentation method Morfessor Baseline to account for the linguistic phenomenon of allomorphy, where one morpheme has several different surface forms. Our method discovers common base forms for allomorphs froman unannotated corpus. We evaluate the method by participating in the Morpho Challenge 2008 competition 1, where inferred analyses are compared against a linguistic gold standard. While our competition entry achieves high precision, but low recall, and therefore low F-measure scores, we show that a small model change gives state-of-the-art results.

Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, that is the surface forms of morphemes. We extend a recent segmentation approach based on conditional random fields from purely supervised to semi-supervised learning by exploiting available unsupervised segmentation techniques. We integrate the unsupervised techniques into the conditional random field model via feature set augmentation. Experiments on three diverse languages show that this straightforward semi-supervised extension greatly improves the segmentation accuracy of the purely supervised CRFs in a computationally efficient manner.

Unsupervised morpheme analysis with allomorfessor

Allomorfessor extends the unsupervised morpheme segmentation method Morfessor to account for the linguistic phenomenon of allomorphy, where one morpheme has several different surface forms. The method discovers common base forms for allomorphs from an unannotated corpus by finding small modifications, called mutations, for them. Using Maximum a Posteriori estimation, the model is able to decide the amount and types of the mutations needed for the particular language. In Morpho Challenge 2009 evaluations, the effect of the mutations was discovered to be rather small. However, Allomorfessor performed generally well, achieving the best results for English in the linguistic evaluation, and being in the top three in the application evaluations for all languages.

A comparative study of minimally supervised morphological segmentation

This article presents a comparative study of a subfield of morphology learning referred to as minimally supervised morphological segmentation. In morphological segmentation, word forms are segmented into morphs, the surface forms of morphemes. In the minimally supervised data-driven learning setting, segmentation models are learned from a small number of manually annotated word forms and a large set of unannotated word forms. In addition to providing a literature survey on published methods, we present an in-depth empirical comparison on three diverse model families, including a detailed error analysis. Based on the literature survey, we conclude that the existing methodology contains substantial work on generative morph lexicon-based approaches and methods based on discriminative boundary detection. As for which approach has been more successful, both the previous work and the empirical evaluation presented here strongly imply that the current state of the art is yielded by the discriminative boundary detection methodology.

Evolution of User Driven Innovation

The source and driver of user driven innovation is a profound understanding of customer needs. Three main approaches to user driven innovation exist: a traditional sequential approach, a lead user approach and customer co-creation. The overall trend is toward increasing user participation throughout the innovation process. Today the leading companies successfully engage users into creative processes of their innovation activities starting in the early stages. In the energy sector user driven innovation methodologies appear promising, in particular as a mean to improve energy efficiency and save energy. This paper focuses on the evolution of user driven innovation. We present an ontology of user driven innovation. It is followed by a state-of-the-art analysis of traditional and new approaches. Finally we try to predict whether a user driven innovation approach could aid the energy sector in overcoming challenges related to global warming and oil shortage.© 2010 ASME

The interplay between cognitive, conative, and affective constructs along the entrepreneurial learning process

Purpose Although the role of reflections in entrepreneurship education is undeniable, the research has focused mainly on their advantages and consequences for learning process, whereas their dynamics and interrelations with other mental processes remain unexplored. The purpose of this paper is to better understand how personality and intelligence constructs: cognition, conation, and affection evolve and change along the learning process during entrepreneurship education. Design/methodology/approach To better understand reflective processes in entrepreneurial learning this paper adopts the tripartite constructs of personality and intelligence. By employing longitudinal explorative research approach and self-organizing map (SOM) algorithm, the authors follow students’ reflections during their two-year learning processes. First, the authors try to identify how the interplay between the cognitive, conative, and affective aspects emerges in students’ reflections. Then, the authors investigate how this interplay evolves during the individual learning process and finally, by looking for similarities in these learning pathways, the authors aim to identify patterns of students’ reflective learning process. Findings All constructs are present during the learning process and all are prone to change. The individual constructs alone shed no light on the interplay between different constructs, but rather that the interplay between sub-constructs should be taken into consideration as well. This seems to be particularly true for cognition, as procedural and declarative knowledge have very different profiles. Procedural knowledge emerges together with emotions, motivation, and volition, whereas the profile of declarative knowledge is individual. The unique profile of declarative knowledge in students’ reflections is an important finding as declarative knowledge is regarded as the center of current pedagogic practices. Research limitations/implications The study broadens the understanding of reflective practices in the entrepreneurial learning process and the interplay between affective, cognitive, and conative sub-constructs and reflective practices in entrepreneurship education. The findings clearly indicate the need for further research on the interplay between sub-constructs and students’ reflection profiles. The authors see the study as an attempt to apply an exploratory statistical method for the problem in question. Practical implications The results are able to advise pedagogy. Practical implications concern the need to develop reflective practises in entrepreneurial learning interventions to enhance all three meta-competencies, even though there are so far no irrefutable findings to indicate that some types of reflection may be better than others. Originality/value The results of the analysis indicate that it is possible to study the complex and dynamic interplay between sub-constructs of cognitive, conative and affective constructs. Moreover, the research succeeded in identifying both individual variations and general reflection patterns and changes in these during the learning process. This was possible by adopting a longitudinal explorative research approach with SOM analyses.

Identifying anomalous social contexts from mobile proximity data using binomial mixture models

Mobile proximity information provides a rich and detailed view into the social interactions of mobile phone users, allowing novel empirical studies of human behavior and context-aware applications. In this study, we apply a statistical anomaly detection method based on multivariate binomial mixture models to mobile proximity data from 106 users. The method detects days when a persons social context is unexpected, and it provides a clustering of days based on the contexts. We present a detailed analysis regarding one user, identifying days with anomalous contexts, and potential reasons for the anomalies. We also study the overall anomalousness of peoples social contexts. This analysis reveals a clear weekly oscillation in the predictability of the contexts and a weekend-like behavior on public holidays.