user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exponential Family Word Embeddings: An Iterative Approach for Learning Word Vectors

Paper content:
GloVe and Skip-gram word embedding methods learn word vectors by decomposing a denoised matrix of word co-occurrences into a product of low-rank matrices.
In this work, we propose an iterative algorithm for computing word vectors based on modeling word co-occurrence matrices with Generalized Low Rank Models.
Our algorithm generalizes both Skip-gram and GloVe as well as giving rise to other embedding methods based on the specified co-occurrence matrix, distribution of co-occurences, and the number of iterations in the iterative algorithm.
For example, using a Tweedie distribution with one iteration results in GloVe and using a Multinomial distribution with full-convergence mode results in Skip-gram.
Experimental results demonstrate that multiple iterations of our algorithm improves results over the GloVe method on the Google word analogy similarity task.
Word embeddings are low dimensional vector representations of words or phrases.
They are applied to word analogy tasks and used as feature vectors in numerous tasks within natural language processing, computational linguistics, and machine learning.
They are constructed by various methods which rely on the distributional hypothesis popularized by Firth: "words are characterized by the company they keep" BID9 .
Two seminal methodological approaches to finding word embeddings are Skip-gram [Mikolov et al., 2013a] and GloVe [Pennington et al., 2014] .
Both methods input a corpus D, process it into a word co-occurence matrix X, then output word vectors with some dimension d.Skip-gram processes a corpus with w words into a count co-occurence matrix X ∈ R w×w , where x ij is the number of times word w i appears in the same context as the word w j .
Here, two words being in the same context means that they're within l c tokens of each other.
Define this co-occurence matrix to be the count co-occurence matrix.
Next, Skip-gram [Pennington et al., 2014 where u u u T i is the i th row of U , then defines the word vectors to be the rows ofÛ .GloVe
processes a corpus with w words into a harmonic co-occurence matrix X ∈ R w×w where x ij is the harmonic sum of the number of tokens between words w i and w j over each co-occurrence. That
is, x ij = p1<p2,|p1−p2|≤lc,D(p1)=wi,D(p2)=wj h(x ij ) u u u DISPLAYFORM0 where a i and b j are bias terms, h(x ij ) = (min{x ij , x max }) .75 is the weight, and x max is some prespecified cutoff. GloVe
then defines the estimated word vectors to be the rows of 1 2Û + 1 2V . In both
Skip-gram and GloVe, a matrix of co-occurences X is introduced by processing the corpus, and an objective function is introduced to find a low rank factorization related to the co-occurences X. In this paper, we derive the objective functions from a model-based perspective. We introduce
an iterative algorithm, and show that problem (1) results from running the iterative algorithm on full-convergence mode for a Multinomial model and problem (2) is one step of the iterative algorithm for a Tweedie model. This algorithm
additionally allows us to introduce methods to "fill in the gaps" between Skip-gram and GloVe and to introduce altogether new methods for finding word vectors.
We present a general model-based methodology for finding word vectors from a corpus.
This methodology involves choosing the distribution of a chosen co-occurrence matrix to be an exponential dispersion family and choosing the number of iterations to run our algorithm.In Table 1 , we see that our methodology unifies the dominant word embedding methods available in the literature and provides new and improved methods.
We introduce an extension of Skip-gram that is stopped before full-convergence analagously to GloVe and an extension to GloVe beyond one iteration.
Experimental results on a small corpus demonstrate our method improves upon GloVe and Skip-gram on the Google word analogy similarity task.
It is our hope that this methodology can lead to the development of better, more statistically sound, word embeddings and consequently improve results on many other downstream tasks.

assistant: text: We present a novel iterative algorithm based on generalized low rank models for computing and interpreting word embedding models.