Sentence alignment of Hungarian-English parallel corpora using a hybrid algorithm

We present an efficient hybrid method for aligning sentences with their translations in a parallel bilingual corpus. The new algorithm is composed of a length-based and anchor matching method that uses Named Entity recognition. This algorithm combines the speed of length-based models with the accuracy of anchor finding methods. The accuracy of finding cognates for Hungarian-English language pair is extremely low, hence we thought of using a novel approach that includes Named Entity recognition. Due to the well selected anchors it was found to outperform the best two sentence alignment algorithms so far published for the Hungarian-English language pair

Automated Building of Sentence-Level Parallel Corpus and Chinese-Hungarian Dictionary

Decades of work have been conducted on automated building of parallel corpus and automatic dictionary in the field of natural language processing. However, rarely have any studies been done between high-density character-based languages and medium-density word-based languages due to the lack of resources and fundamental linguistic differences. In this paper, we describe a methodology for creating a sentence-level paralleled corpus and an automatic bilingual dictionary between Chinese (a high-density character-based language) and Hungarian (a medium-density word-based language). This method will possibly be applied to create Chinese-Hungarian bilingual dictionary for the Sztaki Dictionary project [http://szotar.sztaki.hu/]

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

A free/open-source hybrid morphological disambiguation tool for Kazakh

This paper presents the results of developing a morphological disambiguation tool for Kazakh. Starting with a previously developed rule-based approach, we tried to cope with the complex morphology of Kazakh by breaking up lexical forms across their derivational boundaries into inflectional groups and modeling their behavior with statistical methods. A hybrid rule-based/statistical approach appears to benefit morphological disambiguation demonstrating a per-token accuracy of 91% in running text

Hungarian noun phrase extraction using rule-based and hybrid methods

We implement and revise Kornai's grammar of Hungarian NPs [11] to create a parser that identifies noun phrases in Hungarian text. After making several practical amendments to our morphological annotation system of choice, we proceed to formulate rules to account for some specific phenomena of the Hungarian language not covered by the original rule system. Although the performance of the final parser is still inferior to state-of-the-art machine learning methods, we use its output successfully to improve the performance of one such system