Episode #125 of the Stack Overflow podcast is here. We talk Tilde Club and mechanical keyboards. Listen now

# Tag Info

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My first question would be, why would you use deep learning for tokenization (performance and accuracy-wise)? Apart from some corner cases it's a pretty straightforward task. Existing statistics-based systems and rule-based methods are fast and quite accurate, unlike deep learning approaches which are computationally expensive and require large datasets to ...

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That Wikipedia sentence is worded in such a way that the correct parse is somewhat ambiguous. Here's how I might reword the definition: The PMI of $x$ and $y$ quantifies the discrepancy between the following two probabilities: the probability of $x$ and $y$ under their true joint distribution, which is $p(x,y)$ the probability of $x$ and $y$ ...

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Following is the link for the Python wrapper for CMU tweet tagger, https://github.com/ianozsvald/ark-tweet-nlp-python Its a Simple Python wrapper around runTagger.sh of ark-tweet-nlp. It passes a list of tweets to runTagger.sh and parses the result into a list of lists of tuples, each tuple represents the (token, type, confidence). Download the ark-tweet-...

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I guess the reason why the specific terms "query", "key" and "value" were chosen is that this attention mechanism resembles a memory access mechanism. The query is the specific element for which we seek a representation. The role of the keys is to respond more or less to the query and the values are here to compose an answer. Keys and values are necessarily ...

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It depends on what language are you working on and on what task are you trying to solve Lemmatization would more likely help in topic modeling task, as suggested on: https://opendatagroup.github.io/data%20science/2019/03/21/preprocessing-text.html Topic modeling, for example, relies on the distribution of content words, the identification of which is ...

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Your questions mostly concern the implementation of Fasttext rather than the underlying statistical concepts. I couldn't find clear documentation on how sentence embeddings are calculated from the words embeddings, but looking at the C code provided some hints. The doc string for get_sentence_vector [here][https://github.com/facebookresearch/fastText/blob/...

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