Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models

Recent years have seen a growing interest within the natural language processing (NLP)community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to de-code brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor process-ing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing sup-port for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension

Neural Networks for Language Modeling and Related Tasks in Low-Resourced Domains and Languages. Tehisn\ue4rviv\uf5rgud keele modelleerimise ja sellega seotud \ufclesannete jaoks v\ue4heste ressurssidega valdkondades ja keeltes

Doctoral thesis 55/2018 Doktorit\uf6\uf6 55/201

How to create order in large closed subsets of WordNet-type dictionaries

<p><span>This article presents a new two-step method to handle and study large closed subsets of WordNet-type dictionaries with the goal of finding possible structural inconsistencies. The notion of closed subset is explained using a WordNet tree. A novel and very fast method to order large relational systems is described and compared with some other fast methods. All the presented methods have been tested using Estonian1 and Princeton WordNet2 largest closed sets.</span></p><p>DOI: http://dx.doi.org/10.5128/ERYa9.10</p

Modeling under-resourced languages for speech recognition

One particular problem in large vocabulary continuous speech recognition for low-resourced languages is finding relevant training data for the statistical language models. Large amount of data is required, because models should estimate the probability for all possible word sequences. For Finnish, Estonian and the other fenno-ugric languages a special problem with the data is the huge amount of different word forms that are common in normal speech. The same problem exists also in other language technology applications such as machine translation, information retrieval, and in some extent also in other morphologically rich languages. In this paper we present methods and evaluations in four recent language modeling topics: selecting conversational data from the Internet, adapting models for foreign words, multi-domain and adapted neural network language modeling, and decoding with subword units. Our evaluations show that the same methods work in more than one language and that they scale down to smaller data resources.Peer reviewe