Traducción automática basada en tectogramática para inglés-español e inglés-euskara

Presentamos los primeros sistemas de traducción automática para inglés-español e inglés-euskara basados en tectogramática. A partir del modelo ya existente inglés-checo, describimos las herramientas para el análisis y síntesis, y los recursos para la trasferencia. La evaluación muestra el potencial de estos sistemas para adaptarse a nuevas lenguas y dominios.We present the first attempt to build machine translation systems for the English-Spanish and English-Basque language pairs following the tectogrammar approach. Based on the English-Czech system, we describe the language-specific tools added in the analysis and synthesis steps, and the resources for bilingual transfer. Evaluation shows the potential of these systems for new languages and domains.The research leading to these results has received funding from FP7-ICT-2013-10-610516 (QTLeap project, qtleap.eu)

Automated Translation with Interlingual Word Representations

In dit proefschrift onderzoeken we het gebruik vertaalsystemen die gebruiken maken van een transferfase met interlinguale representaties van woorden. Op deze manier benaderen we het probleem van de lexicale ambiguïteit in de automatische vertaalsystemen als twee afzonderlijke taken: het bepalen van woordbetekenis en lexicale selectie. Eerst worden de woorden in de brontaal op basis van hun betekenis gedesambigueerd, resulterend in interlinguale representaties van woorden. Vervolgens wordt een lexicale selectiemodule gebruikt die het meest geschikte woord in de doeltaal selecteert. We geven een gedetailleerde beschrijving van de ontwikkeling en evaluatie van vertaalsystemen voor Nederlands-Engels. Dit biedt een achtergrond voor de experimenten in het tweede en derde deel van dit proefschrift. Daarna beschrijven we een methode die de betekenis van woorden bepaalt. Deze is vergelijkbaar met het klassieke Lesk-algoritme, omdat het gebruik maakt van het idee dat gedeelde woorden tussen de context van een woord en zijn definitie informatie over de betekenis ervan verschaffen. Wij gebruiken echter, in plaats daarvan, woord- en betekenisvectoren om de overeenkomst te berekenen tussen de definitie van een betekenis en de context van een woord. We gebruiken onze methode bovendien voor het localiseren en -interpreteren van woordgrapjes.Ten slotte presenteren we een model voor lexicale keuze dat lemma's selecteert, gegeven de abstracte representaties van woorden. Dit doen we door de grammaticale bomen te converteren naar hidden Markov bomen. Op deze manier kan de optimale combinatie van lemmas en hun context berekend worden

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

Edinburgh's Statistical Machine Translation Systems for WMT16

This paper describes the University of Edinburgh’s phrase-based and syntax-based submissions to the shared translation tasks of the ACL 2016 First Conference on Machine Translation (WMT16). We submitted five phrase-based and five syntaxbased systems for the news task, plus one phrase-based system for the biomedical task

Findings of the 2016 Conference on Machine Translation.

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries

Extrakce znalostních grafů z projektové dokumentace

Název práce: Extrakce znalostních grafů z projektové dokumentace Autor: Bc. Tomáš Helešic Katedra: Katedra softwarového inženýrství Vedoucí diplomové práce: Mgr. Martin Nečaský, Ph.D. Abstrakt: Cílem této práce je prozkoumat možnosti automatické extrakce infor- mací z firemní projektové dokumentace s využitím nástroje pro strojové zpra- cování přirozeného jazyka a analýza přesnosti lingvistického zpracování těchto dokumentů. Dále navrhnout metody, jak získat klíčové pojmy a vazby mezi nimi. Z těchto pojmů a vazeb se vytváří znalostní grafy, které se uchovávají ve vhodném úložisti s vyhledávací službou. Práce se snaží propojit již ex- istující technologie, implementovat je do jednoduché aplikace a ověřit jejich připravenost pro praktické využití. Cílem je inspirovat budoucí výzkum v této oblasti, identifikovat kritická místa a navhrnout zlepšení. Hlavní přínos tkví v propojení zpracování přirozeného jazyka, metod extrakce informací, sémantické vyhledávání s firemnímy dokumenty. Přínos praktické části spočívá ve způsobu identifikace důležitých informací, které popisují jednotlivé dokumenty a jejich využití ve vyhledávání. Klíčová slova: Znalostní grafy, Extrakce informace, Zpracování...Title: Knowledge Graph Extraction from Project Documentation Author: Bc. Tomáš Helešic Department: Department of Software Engineering Supervisor: Mgr. Martin Nečaský, Ph.D. Abstract: The goal of this thesis is to explore the possibilities of automatic in- formation extraction from company project documentation with the use of ma- chine natural language processing and the analysis of the precision of linguistic processing of these documents. Furthermore suggest methods how acquire key terms and dependencies between them. From this terms and dependencies cre- ate knowledge graphs, that are stored in an appropriate database with search engine. The work is trying to interconnect already existing technologies in a shape of a simple application and test their readiness for a practical use. The goal is to inspire future research in this field, identify critical parts and propose improvements. The main gain is in the interconnection between natural lan- guage processing, methods of information extraction and semantic searching in corporate documents. The gain of the practical part reside in the way how to identify key information that is uniquely describing each document and its use in search. Keywords: Knowledge graphs, Information extraction, Natural language pro- cessing, Resource Description Framework 1Katedra softwarového inženýrstvíDepartment of Software EngineeringMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Adaptation of machine translation for multilingual information retrieval in the medical domain

Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR. Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech–English, German–English, and French–English. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets. Results. The search query translation results achieved in our experiments are outstanding – our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech–English, from 23.03 to 40.82 for German–English, and from 32.67 to 40.82 for French–English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French–English. For Czech–English and German–English, the increased MT quality does not lead to better IR results. Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance – better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions