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Results tagged with nlp
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Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.
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Accepted
What does localist one-hot vector mean in cs224n NLP course?
One-hot vector is called "localist" because it contains information only about a single data point, and does not give clues about other points, in contrast to a distributed representation (e.g. result …
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1
vote
Accepted
Why Heaps' Law Equation looks so different in this NLP course?
The plot shows Heaps' Law but the formula is something different, it is Zipf's Law.
$f(w)$ is the relative frequency (or probability) of word $w$. That is, given a random word, it will be $w$ with p …
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3
votes
Approaching a multi-class classification problem but without labels
Your specific problem can be solved by Googling.
Here is a solution that
Searches the "imdb [year] [movie name]" in Google,
Finds its IMDb address and fetches the IMDb page, and then
Searches fo …
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4
votes
Accepted
Why does all of NLP literature use Noise contrastive estimation loss for negative sampling i...
Both negative sampling (derived from NCE) and sampled SoftMax use a few samples to bypass the calculation of full SoftMax.
The main problem comes from this comment in the linked pdf:
Sampled Softmax
…
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70
votes
What is the positional encoding in the transformer model?
For example, for word $w$ at position $pos \in [0, L-1]$ in the input sequence $\boldsymbol{w}=(w_0,\cdots, w_{L-1})$, with 4-dimensional embedding $e_{w}$, and $d_{model}=4$, the operation would be
$ …
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14
votes
Word2Vec how to choose the embedding size parameter
I have checked four well-cited papers related to word embedding: 2013 Word2Vec, 2014 GloVe, 2018 BERT, and 2018 ELMo. Only GloVe has experimented on the embedding dimension for the analogy task (answe …
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1
vote
Accepted
The principle of LM deep model
The goal of LM is to learn a probability distribution over sequences
of symbols pertaining to a language.
That is, to learn $P(w_1,...,w_N)$ (resource).
This modeling can be accomplished by
…
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