Handwriting recognition systems rely on predefined classifiers. Small and static dictionaries are usually exploited to obtain high in-vocabulary (IV) accuracy at the expense of coverage. Thus the recognition of out-of-vocabulary (OOV) words cannot be handled efficiently. To improve OOV recognition while keeping IV dictionaries small, we introduce a multi-step approach that exploits Web resource...

German is a highly inflectional language, where a large number of words can be generated from the same root. It makes a liberal use of compounding leading to high Out-of-vocabulary (OOV) rates, and poor Language Model (LM) probability estimates. Therefore, the use of morphemes for language modeling is considered a better choice for Large Vocabulary Continuous Speech Recognition (LVCSR) than the...

In this paper, we introduce a new syllabic model for handwriting recognition. We propose a supervised syllabification approach of the French language for building a vocabulary of syllables. A statistical n-gram language model of syllables is trained on a Wikipedia corpus. The handwriting recognition system, based on optical character HMM, performs a two pass decoding, integrating the proposed s...

Since the emergence of translation memory software, translation companies and freelance translators have been accumulating translated text for various languages and domains. This data has the potential of being used for training domain-specific machine translation systems for corporate or even personal use. But while the resulting systems usually perform well in translating domain-specific lang...

This paper explores one dimension along which word spotting and speech recognition differ: the nature of the background model. In word spotting, a relatively small number of keywords float on a sea of unknown words. In speech recognition, an occasional unknown word punctuates utterances that are otherwise completely invocabulary. Despite this difference in viewpoint, in some circumstances imple...

BACKGROUND The widely known terminology gap between health professionals and health consumers hinders effective information seeking for consumers. OBJECTIVE The aim of this study was to better understand consumers' usage of medical concepts by evaluating the coverage of concepts and semantic types of the Unified Medical Language System (UMLS) on diabetes-related postings in 2 types of social ...

We propose WordNews, a web browser extension that allows readers to learn a second language vocabulary while reading news online. Injected tooltips allow readers to look up selected vocabulary and take simple interactive tests. We discover that two key system components needed improvement, both which stem from the need to model context. These two issues are real-world word sense disambiguation ...

In natural language processing many practical tasks, such as speech recognition, information retrieval and machine translation depend on a large vocabulary and statistical language models. For morphologically rich languages, such as Finnish and Turkish, the construction of a vocabulary and language models that have a sufficient coverage is particularly difficult, because of the huge amount of d...

In the field of developmental disorders, there is no commonly accepted medical vocabulary. Vocabularies, such as ICD-10, are unsatisfying to clinicians, who try to create their own diagnostic lists. This results in inconsistency in the terms used in clinical practice. When attempting to apply automatic computational methods on patients' data, the need for common consistent diagnoses list arises...

We present an overview of Regulus, an Open Source platform that supports corpus-based derivation of efficient domain-specific speech recognisers from general linguistically motivated unification grammars. We list available Open Source resources, which include compilers, resource grammars for various languages, documentation and a development environment. The greater part of the paper presents a...