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. […]

Inverse Document Frequency (IDF) is a weight indicating how commonly a word is used. The more frequent its usage across documents, the lower its score. The lower the score, the less important the word becomes. For example, the word the appears in almost all English texts and would thus have a very low IDF score as it carries very little “topic” information. In contrast, if you take the word coffee, while it is common, it’s not used as widely as […]

Based on some recent conversations, I realized that text preprocessing is a severely overlooked topic. A few people I spoke to mentioned inconsistent results from their NLP applications only to realize that they were not  preprocessing their text or were using the wrong kind of text preprocessing for their project. With that in mind, I thought of shedding some light around what text preprocessing really is, the different techniques of text preprocessing and a way to estimate how much preprocessing […]