In the previous sections, we have shown how syntactic nuclei can be identified in the UD annotation and how transition-based parsers can be made sensitive to these structures in their internal representations through the use of nucleus composition. We now proceed to a set of experiments investigating the impact of nucleus composition on a diverse selection of languages. 5.1 Experimental Settings We use UUParser (de Lhoneux et al., 2017, Smith    

With the aim of assessing the quality of the trained SE models, we use several trigger word detection classifier models, reporting the impact of the SE module at WUW classification performance. The WUW classifiers used here are a LeNet, a well-known standard classifier, easy to optimize [13]; Res15, Res15-narrow and Res8 based on a reimplementation by Tang and Lin [26] of Sainath and Parada’s Convolutional Neural Networks (CNNs) for    

Having spent a big part of my career as a graduate student researcher and now a Data Scientist in the industry, I have come to realize that a vast majority of solutions proposed both in academic research papers and in the work place are just not meant to ship — they just don’t scale! And when I say scale, I mean handling real world uses cases,  ability to handle large amounts of data and ease of deployment in a production […]

N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios). For example, for the sentence “The cow jumps over the moon”. If N=2 (known as bigrams), then the ngrams would be: the cow cow jumps jumps over over the the moon So […]

Term Frequency (TF) Term frequency (TF) often used in Text Mining, NLP and Information Retrieval tells you how frequently a term occurs in a document. In the context natural language, terms correspond to words or phrases. Since every document is different in length, it is possible that a term would appear more often in longer documents than shorter ones. Thus, term frequency is often divided by the  the total number of terms in the document as a way of normalization. […]