NLP Applications

AbstractThe paper reports on completed work aimed at the creation of a resource, namely, the Greek Textual Entailment Corpus (GTEC) that is appropriate for guiding training and evaluation of a …...

AbstractDespite Arabic is the language of hundred millions of people over the world, little has been done in terms of computerized linguistic resources, tools or applications. In this paper we …...

AbstractThis paper presents the development of an open-source Spanish Dependency Grammar implemented in FreeLing environment. This grammar was designed as a resource for NLP applications that require a step further …...

. Semantic lexical resources are a mainstay of various NLP applications. However, comprehensive and reliable resources rarely exist or are often not freely available. We discuss in this paper the …...

Many natural language processing problems involve constructing large nearest-neighbor graphs. We propose a system called FLAG to construct such graphs approximately from large data sets. To handle the large amount …...

AbstractRecent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per …...

AbstractLight verb constructions (LVC) in Hindi are highly productive. If we can distinguish a case such as nirnay lenaa ‘decision take; decide’ from an ordinary verb-argument combination kaagaz lenaa ‘paper …...

AbstractGiven an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications. Existing work cannot well represent the heterogeneous relations and …...

AbstractContextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances. However, their opaqueness makes it hard to interpret their behaviour. …...

AbstractUnsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, …...

Data augmentation has recently seen increased interest in NLP due to more work in low- resource domains, new tasks, and the popu- larity of large-scale neural networks that re- quire …...