Rječnik suvremenoga slovenskog jezika: od slovenske leksičke baze do digitalne rječničke baze

The ability to process language data has become fundamental to the development of technologies in various areas of human life in the digital world. The development of digitally readable linguistic resources, methods, and tools is, therefore, also a key challenge for the contemporary Slovene language. This challenge has been recognized in the Slovene language community both at the professional and state level and has been the subject of many activities over the past ten years, which will be presented in this paper. The idea of a comprehensive dictionary database covering all levels of linguistic description in modern Slovene, from the morphological and lexical levels to the syntactic level, has already formulated within the framework of the European Social Fund’s Communication in Slovene (2008-2013) project; the Slovene Lexical Database was also created within the framework of this project. Two goals were pursued in designing the Slovene Lexical Database (SLD): creating linguistic descriptions of Slovene intended for human users that would also be useful for the machine processing of Slovene. Ever since the construction of the first Slovene corpus, it has become evident that there is a need for a description of modern Slovene based on real language data, and that it is necessary to understand the needs of language users to create useful language reference works. It also became apparent that only the digital medium enables the comprehensiveness of language description and that the design of the database must be adapted to it from the start. Also, the description must follow best practices as closely as possible in terms of formats and international standards, as this enables the inclusion of Slovene into a wider network of resources, such as Open Linked Data, babelNet and ELExIS. Due to time pressures and trends in lexicography, procedures to automate the extraction of linguistic data from corpora and the inclusion of crowdsourcing into the lexicographic process were taken into consideration. Following the essential idea of creating an all-inclusive digital dictionary database for Slovene, a few independent databases have been created over the past two years: the Collocations Dictionary of Modern Slovene, and the automatically generated Thesaurus of Modern Slovene, both of which also exist as independent online dictionary portals. One of the novelties that we put forward together with both dictionaries is the ‘responsive dictionary’ concept, which includes crowdsourcing methods. Ultimately, the Digital Dictionary Database provides all (other) levels of linguistic description: the morphological level with the Sloleks database upgrade, the phraseological level with the construction of a multi-word expressions lexicon, and the syntactic level with the formalization of Slovene verb valency patterns. Each of these databases contains its specific language data that will ultimately be included in the comprehensive Slovene Digital Dictionary Database, which will represent basic linguistic descriptions of Slovene both for the human and machine user.Ideja sveobuhvatne rječničke baze koja uključuje sve razine jezičnoga opisa suvremenoga slovenskog jezika od morfološke i leksičke do sintaktičke prvotno je formulirana u okviru projekta Sporazumijevanje na slovenskomu jeziku (2008. – 2013.). U cilju ostvarenja ideje o stvaranju sveobuhvatne digitalne rječničke baze stvorene su dvije neovisne baze podataka: Kolokacijski rječnik suvremenoga slovenskoga jezika i automatski generiran Tezaurus modernoga slovenskoga jezika. Jedna od novina u obama rječnicima koncept je responzivnoga rječnika, koji uključuje masovnu podršku. Digitalna rječnička baza sadržava sve razine jezičnoga opisa: morfološku nadograđenu Sloleksom, izraznu s opisom konstrukcija višerječnih jedinica te sintaktičku s formalizacijom modela glagolskih valencija. Svaka od postojećih baza podataka sadržava specifične jezične podatke koji će biti uključeni u sveobuhvatnu Slovensku digitalnu rječničku bazu podataka, koja će sadržavati temeljni jezikoslovni opis slovenskoga jezika čiji korisnici mogu biti ljudi i strojevi

External Lexical Information for Multilingual Part-of-Speech Tagging

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet better performances are obtained with our feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features. This shows that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods

TermEval: an automatic metric for evaluating terminology translation in MT

Terminology translation plays a crucial role in domain-specific machine translation (MT). Preservation of domain-knowledge from source to target is arguably the most concerning factor for the customers in translation industry, especially for critical domains such as medical, transportation, military, legal and aerospace. However, evaluation of terminology translation, despite its huge importance in the translation industry, has been a less examined area in MT research. Term translation quality in MT is usually measured with domain experts, either in academia or industry. To the best of our knowledge, as of yet there is no publicly available solution to automatically evaluate terminology translation in MT. In particular, manual intervention is often needed to evaluate terminology translation in MT, which, by nature, is a time-consuming and highly expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems are often needed to be updated for many reasons (e.g. availability of new training data or leading MT techniques). Hence, there is a genuine need to have a faster and less expensive solution to this problem, which could aid the end-users to instantly identify term translation problems in MT. In this study, we propose an automatic evaluation metric, TermEval, for evaluating terminology translation in MT. To the best of our knowledge, there is no gold-standard dataset available for measuring terminology translation quality in MT. In the absence of gold standard evaluation test set, we semi-automatically create a gold-standard dataset from English--Hindi judicial domain parallel corpus. We trained state-of-the-art phrase-based SMT (PB-SMT) and neural MT (NMT) models on two translation directions: English-to-Hindi and Hindi-to-English, and use TermEval to evaluate their performance on terminology translation over the created gold standard test set. In order to measure the correlation between TermEval scores and human judgments, translations of each source terms (of the gold standard test set) is validated with human evaluator. High correlation between TermEval and human judgements manifests the effectiveness of the proposed terminology translation evaluation metric. We also carry out comprehensive manual evaluation on terminology translation and present our observations

A stemming algorithm for Latvian

The thesis covers construction, application and evaluation of a stemming algorithm for advanced information searching and retrieval in Latvian databases. Its aim is to examine the following two questions: Is it possible to apply for Latvian a suffix removal algorithm originally designed for English? Can stemming in Latvian produce the same or better information retrieval results than manual truncation? In order to achieve these aims, the role and importance of automatic word conflation both for document indexing and information retrieval are characterised. A review of literature, which analyzes and evaluates different types of stemming techniques and retrospective development of stemming algorithms, justifies the necessity to apply this advanced IR method also for Latvian. Comparative analysis of morphological structure both for English and Latvian language determined the selection of Porter's suffix removal algorithm as a basis for the Latvian sternmer. An extensive list of Latvian stopwords including conjunctions, particles and adverbs, was designed and added to the initial sternmer in order to eliminate insignificant words from further processing. A number of specific modifications and changes related to the Latvian language were carried out to the structure and rules of the original stemming algorithm. Analysis of word stemming based on Latvian electronic dictionary and Latvian text fragments confirmed that the suffix removal technique can be successfully applied also to Latvian language. An evaluation study of user search statements revealed that the stemming algorithm to a certain extent can improve effectiveness of information retrieval

Towards a machine-learning architecture for lexical functional grammar parsing

Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages. The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing. In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages

Language processing infrastructure in the XLike project

This paper presents the linguistic analysis tools and its infrastructure developed within the XLike project. The main goal of the implemented tools is to provide a set of functionalities for supporting some of the main objectives of XLike, such as enabling cross-lingual services for publishers, media monitoring or developing new business intelligence applications. The services cover seven major and minor languages: English, German, Spanish, Chinese, Catalan, Slovenian, and Croatian. These analyzers are provided as web services following a lightweight SOA architecture approach, and they are publically callable and are catalogued in META-SHAREPostprint (published version