<story><title>How to solve most NLP problems: a step-by-step guide</title><url>https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e</url></story><parent_chain></parent_chain><comment><author>minimaxir</author><text>Word2Vec and bag-of-words&amp;#x2F;tf-idf are somewhat obsolete in 2018 for modeling. For classification tasks, fasttext (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;facebookresearch&amp;#x2F;fastText&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;facebookresearch&amp;#x2F;fastText&lt;/a&gt;) performs better and faster.&lt;p&gt;Fasttext is also available in the popular NLP Python library gensim, with a good demo notebook: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;radimrehurek.com&amp;#x2F;gensim&amp;#x2F;models&amp;#x2F;fasttext.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;radimrehurek.com&amp;#x2F;gensim&amp;#x2F;models&amp;#x2F;fasttext.html&lt;/a&gt;&lt;p&gt;And of course, if you have a GPU, recurrent neural networks (or other deep learning architectures) are the endgame for the remaining 10% of problems (a good example is SpaCy&amp;#x27;s DL implementation: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;spacy.io&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;spacy.io&amp;#x2F;&lt;/a&gt;). Or use those libraries to incorporate fasttext for text encoding, which has worked well in my use cases.</text></comment>
<story><title>How to solve most NLP problems: a step-by-step guide</title><url>https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e</url></story><parent_chain></parent_chain><comment><author>paulsutter</author><text>NLP is one of the most challenging areas of research, and nothing in this article will help solve even 0.009% of those challenges&lt;p&gt;Example of the wisdom herein:&lt;p&gt;&amp;gt; Remove words that are not relevant, such as “@” twitter mentions or urls</text></comment>