Морфологиялық Таңбалау

AbstractA finite-state, rule-based morphological analyser is presented here, within the framework of machine translation system TAVAL. This morphological analyser introduces specific features which are particularly useful for translation, such as …...

We present some preliminary results of a Czech-English translation system based on dependency trees. The fully auto- mated process includes: morphological tagging, analytical and tectogrammat- ical parsing of Czech, tectogrammati- …...

AbstractThe paper describes the project held within Russian National Corpus (http://www.ruscorpora.ru). Beside such obligatory constituents of a linguistic corpus as POS (parts of speech) and morphological tagging RNC contains semantic …...

Training higher-order conditional fields is prohibitive for huge tag sets.random We present an approximated conditional random field using coarse-to-fine decoding and early updating. We show that our implementation yields fast …...

AbstractThe paper overviews the SYN series of synchronic corpora of written Czech compiled within the framework of the Czech National Corpus project. It describes their design and processing with a …...

We present an approach to Arabic auto- matic diacritization that integrates syntac- tic analysis with morphological tagging through improving the prediction of case and state features. Our best system in- …...

In this paper we explore a POS tagging ap- plication of neural architectures that can infer word representations from the raw character stream. It relies on two mod- elling stages …...

AbstractThis paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarely- and unseen words gracefully. We …...

AbstractEven for common NLP tasks, sufficient supervision is not available in many languages – morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, …...

AbstractNeural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the …...

This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small …...

We present TIGER 2.2-doc – a new set of annotations for the German TIGER corpus. The set moves the corpus to a document level. It includes a full mapping of …...

AbstractUser-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for …...

AbstractEnglish verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse …...

AbstractMorphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal variants of morphologically rich …...

AbstractIn the paper we present the latest changes introduce to Inforex — a web-based system for qualitative and collaborative text corpora annotation and analysis. One of the most important news …...

AbstractRecent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in …...

AbstractAdapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. …...

The grammatical analysis of texts in any written language typically involves a num- ber of basic processing tasks, such as tok- enization, morphological tagging, and de- pendency parsing. State-of-the-art systems …...