39,404 research outputs found
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like , indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets.Comment: 5 pages, 3 figures. Rep4NLP workshop at ACL 201
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Language Modeling by Clustering with Word Embeddings for Text Readability Assessment
We present a clustering-based language model using word embeddings for text
readability prediction. Presumably, an Euclidean semantic space hypothesis
holds true for word embeddings whose training is done by observing word
co-occurrences. We argue that clustering with word embeddings in the metric
space should yield feature representations in a higher semantic space
appropriate for text regression. Also, by representing features in terms of
histograms, our approach can naturally address documents of varying lengths. An
empirical evaluation using the Common Core Standards corpus reveals that the
features formed on our clustering-based language model significantly improve
the previously known results for the same corpus in readability prediction. We
also evaluate the task of sentence matching based on semantic relatedness using
the Wiki-SimpleWiki corpus and find that our features lead to superior matching
performance
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