New answers tagged natural-language-processing
1
The good folks behind Spacy have their paid product called Prodigy which is a data labeling tool. I haven't used it but it appears you can host it somewhere and then you would just have to send the link to the students. It is a little pricey but you get a lifetime license...
A free alternative might be Label Studio but I am not sure how easy it is to host it ...
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Look for sequence-to-sequence modelling, aka, seq2seq.
https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html
https://en.wikipedia.org/wiki/Seq2seq
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This is simply overfitting. The model is performing well on train data but bad on test (unseen) data; you can measure it also by noticing a huge difference between the training accuracy and the validation accuracy. Of course this is not a natural behavior, to solve this you need to apply some data or network modifications in order to avoid overfitting. Some ...
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There are a handful of tools available for manually comparing pronounciations, though all are limited in some way. Depending on your usecase, you might be interested in:
Wikspeak: a tool that transcribes (single) words into IPA and generates a pronounciation. A web demo is available, though it’s a bit sensitive about browser versions.
espeak-ng: provides a ...
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A very simple approach can be:
Calculate tf-idf vector for sentence 1 and 2.
Calculate vector similarity (Cosine similarity) of these 2 vectors.
This is a general approach and works for any representational vector.
For a more complex one, check semantic similarity with BERT post from Keras blog.
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$P(d)$ (aka evidence) is a probability of your data (observation) and is defined as follows:
$$
P(d) = \sum_i P(d|c_i)P(c_i)
$$
for all classes $c$.
According to the book, $P(c)=\frac{N_c}{N_{doc}}$ and $P(d|c)$ is a likelihood and, applying the assumptions from the book, can be defined as $P(w_i
|c)=\frac{\text{count}(w_i, c)+1}{\sum_{w \in V}\text{count}(w,...
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I think that these terms may be used inconsistently across sources.
If someone says held-out dataset, I would immediately think of a dataset that is not used for training, but can be used for anything else, validation (hyper-parameter tuning or early stopping) or testing; so, to determine what they are referring to, I would probably take into account the ...
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