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Detecting lexical entailment plays a fundamental role in a variety of natural language processing tasks and is key to language understanding. Unsupervised methods still play an important role due to the lack of coverage of lexical databases in some domains ...
Different senses of source words must often be rendered by different words in the target language when performing machine translation (MT). Selecting the correct translation of polysemous words can be done based on the contexts of use. However, state-of-th ...
Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to
its strong capabilities to encode word content, CBOW embeddings perform well on
a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of ca ...
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on ...
The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simpl ...
Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...
For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...
This paper aims to describe and explain the processes behind the creation of a digital library composed of two Swiss newspapers, namely Gazette de Lausanne (1798-1998) and Journal de Genève (1826-1998), covering an almost two-century period. We developed a ...