The paper reports on the first steps in developing a time-stamped multimodal dataset of reading data by Bulgarian children. Data are being collected, structured and analysed by means of ReadLet, an innovative infrastructure for multimodal language data collection that uses a tablet as a reader’s front-end. The overall goal of the project is to quantitatively analyse the reading skills of a sample of early Bulgarian readers collected over a two-year period, and compare them with the reading data of early readers of Italian, collected using the same protocol. We illustrate design issues of the experimental protocol, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of the Bulgarian dataset for reading research, we present some preliminary statistical analyses of our recently collected data. They show robust convergence trends between Bulgarian and Italian early reading development stages.

This paper offers a perspective of languages with a less significant volume of digital usership as minor in the context of globalization and localization. With this premise, the risks this status poses to the quality of localized texts, the substantiality of genre conventions, the public image of professional translators, and the users’ linguistic competence in these languages is explored. Furthermore, the common lack of established or clear conventions in the localization of digital products into commercially minor languages (and in the digital product genres) is highlighted as one of the factors amplifying these risks. These perspectives are contextualized with the Bulgarian language with examples of errors encountered in Bulgarian digital content localized from English and more specifically – errors and problems related to gender neutrality and register.

The paper presents the ongoing process of compilation of a multilingual corpus of illustrative examples to supplement our work on the syntactic and semantic analysis of predicates representing activities in Bulgarian and other languages. The corpus aims to include over 1,000 illustrative examples on verbs from six semantic classes of predicates (verbs of motion, contact, consumption, creation, competition and bodily functions) which provide a basis for observations on the specificity of their realisation. The corpus of illustrative examples will be used for contrastive studies and further elaboration on the scope and behaviour of activity verbs in general, as well as its semantic subclasses.

The paper presents the work on the selection, semantic annotation and classification of a group of verbs from WordNet, characterized with the semantic primitive ‘verbs of contact’ that belong to the common Bulgarian lexis. The selection of the verb set using both different criteria: statistical information from corpora, WordNet Base concepts and AoA as a criterion, is described. The focus of the work is on the process of the verbs’ of contact semantic annotation using the combined information from two language resources - WordNet and FrameNet. The verbs of contact from WordNet are assigmed semantic frames from FrameNet and then grouped in semantic subclasses using both their place in the WordNet hierarchy, the semantic restrictions on their frame elements and the corresponding syntactic realization. At the end we offer some conclusions on the classification of ‘verbs of contact’ in semantic subtypes.

This paper represents a description of Bulgarian verbal computer terms with a view to the specifics of their translation in English. The study employs a subset of 100 verbs extracted from the Bulgarian WordNet (BulNet) and from the internet. The analysis of their syntactic and semantic structure is a part of a study of the general lexis of Bulgarian. The aim of the paper is to (1) identify some problem areas of the description and translation of general lexis verbs, (2) offer an approach to the semantic description of metaphor-based terms from the perspective of Frame Semantics; (3) raise questions about the definition of general lexis with respect to Bulgarian and across languages.

In this paper we focus on verbal multiword expressions (VMWEs) in Bulgarian and Romanian as reflected in the wordnets of the two languages. The annotation of VMWEs relies on the classification defined within the PARSEME Cost Action. After outlining the properties of various types of VMWEs, a cross-language comparison is drawn, aimed to highlight the similarities and the differences between Bulgarian and Romanian with respect to the lexicalization and distribution of VMWEs. The contribution of this work is in outlining essential features of the description and classification of VMWEs and the cross-language comparison at the lexical level, which is essential for the understanding of the need for uniform annotation guidelines and a viable procedure for validation of the annotation.

This paper presents the principles and procedures involved in the construction of a classification of verbs using information from 3 semantic resources – WordNet, FrameNet and VerbNet. We adopt the FrameNet frames as the primary categories of the proposed classification and transfer them to WordNet synsets. The hierarchical relationships between the categories are projected both from the hypernymy relation in WordNet and from the hierarchy of some of the frame-to-frame relations in FrameNet. The semantic classes and their hierarchical organisation in WordNet are thus made explicit and allow for linguistic generalisations on the inheritance of semantic features and structures. We then select the beginners of the separate hierarchies and assign classification categories recursively to their hyponyms using a battery of procedures based on generalisations over the semantic primes and the hierarchical structure of WordNet and FrameNet and correspondences between VerbNet superclasses and FrameNet frames. The so-obtained suggestions are ranked according to probability. As a result, 13,465 out of 14,206 verb synsets are accommodated in the classification hierarchy at least through a general category, which provides a point of departure towards further refinement of categories. The resulting system of classification categories is initially derived from the WordNet hierarchy and is further validated against the hierarchy of frames within FrameNet. A set of procedures is established to address inconsistencies and heterogeneity of categories. The classification is subject to ongoing extensive manual verification, essential for ensuring the quality of the resource.

This paper presents work in progress focused on developing a method for automatic identification of light verb constructions (LVCs) as a subclass of Bulgarian verbal MWEs. The method is based on machine learning and is trained on a set of LVCs extracted from the Bulgarian WordNet (BulNet) and the Bulgarian National Corpus (BulNC). The machine learning uses lexical, morphosyntactic, syntactic and semantic features of LVCs. We trained and tested two separate classifiers using the Java package Weka and two learning decision tree algorithms – J48 and RandomTree. The evaluation of the method includes 10-fold cross-validation on the training data from BulNet (F1 = 0.766 obtained by the J48 decision tree algorithm and F1 = 0.725 by the RandomTree algorithm), as well as evaluation of the performance on new instances from the BulNC (F1 = 0.802 by J48 and F1 = 0.607 by the RandomTree algorithm). Preliminary filtering of the candidates gives a slight improvement (F1 = 0.802 by J48 and F1 = 0.737 by RandomTree).

This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.

In this paper we present a method for automatic assignment of morphosemantic relations between derivationally related verb–noun pairs of synsets in the Bulgarian WordNet (BulNet) and for semantic filtering of those relations. The filtering process relies on the meaning of noun suffixes and the semantic compatibility of verb and noun taxonomic classes. We use the taxonomic labels assigned to all the synsets in the Princeton WordNet (PWN) – one label per synset – which denote their general semantic class. In the first iteration we employ the pairs <noun suffix : noun label> to filter out part of the relations. In the second iteration, which uses as input the output of the first one, we apply a stronger semantic filter. It makes use of the taxonomic labels of the noun-verb synset pairs observed for a given morphosemantic relation. In this way we manage to reliably filter out impossible or unlikely combinations. The results of the performed experiment may be applied to enrich BulNet with morphosemantic relations and new synsets semi-automatically, while facilitating the manual work and reducing its cost.

Romanian and Bulgarian are Balkan languages with rich derivational morphology that, if introduced into their respective wordnets, can aid broadening of the wordnet content and the possible NLP applications. In this paper we present a joint work on introducing derivation into the Bulgarian and the Romanian WordNets, BulNet and RoWordNet, respectively, by identifying and subsequently labelling the derivationally and semantically related noun-verb pairs. Our research aims at providing a framework for a comparative study on derivation in the two languages and offering training material for the automatic identification and assignment of derivational and morphosemantic relations needed in various applications.