Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple Language Model based on PMI Matrix Approximations   In this study, we introduce a new approach for learning language models by
training them to estimate word-context pointwise mutual information (PMI), and
then deriving the desired conditional probabilities from PMI at test time.
Specifically, we show that with minor modifications to word2vec's algorithm, we
get principled language models that are closely related to the well-established
Noise Contrastive Estimation (NCE) based language models. A compelling aspect
of our approach is that our models are trained with the same simple negative
sampling objective function that is commonly used in word2vec to learn word
embeddings.

31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph