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The UCREL team at the University of Lancaster is engaged in the development of a robust parsing mechanism, which will assign the appro- priate grammatical structure to sentences in unconstrained …...

As the acquisition problem of bilingual lists of terminological expressions is formidable, it is worthwhile to investigate methods to compile such lists as automatically as pos- sible. In this paper …...

In this article, we apply to natural language parsing and tagging the device of trigger- pair predictors, previously employed exclu- sively within the field of language mod- elling for speech …...

AbstractThis paper describes a tool designed to assess the machine translatability of English source texts by assigning a translatability index to both individual sentences and the text as a whole. …...

We describe the use of XML tokenisa- tion, tagging and mark-up tools to pre- pare a corpus for parsing. Our tech- niques are generally applicable but here we focus on …...

In this paper, we describe a resource-light system for the automatic morphological analysis and tag- ging of Russian. We eschew the use of extensive resources (particularly, large annotated corpora and …...

We present PurePos, an open-source HMM-based automatic morphological an- notation tool. PurePos can perform tag- ging and lemmatization at the same time, it is very fast to train, with the …...

AbstractIn this paper, we describe our generic approach for transferring part-of-speech annotations from a resourced language towards an etymologically closely related non-resourced language, without using any bilingual (i.e., parallel) data. …...

Structured perceptron becomes popular for various NLP tasks such as tagging and parsing. Practical studies on NLP did not pay much attention to its regularization. In this paper, we study …...

AbstractWhen producing textual descriptions, humans express propositions regarding an object; but what do they express when annotating a document with simple tags? To answer this question, we have studied what …...

We present LEMMING, a modular log- linear model that jointly models lemmati- zation and tagging and supports the inte- gration of arbitrary global features. It is trainable on corpora annotated …...

Traditional syntax models typically lever- age part-of-speech (POS) information by constructing features from hand-tuned templates. We demonstrate that a better approach is to utilize POS tags as a reg- ularizer …...

AbstractTransfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each …...

AbstractMany NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of easy-negative …...

AbstractIn this paper, we show that neural machine translation (NMT) systems trained on large back-translated data overfit some of the characteristics of machine-translated texts. Such NMT systems better translate human-produced …...