8,161 research outputs found
Learning to distinguish hypernyms and co-hyponyms
This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs of feature vectors with state-of-the-art methods based on distributional similarity. We show that the new supervised approach does better even when there is minimal information about the target words in the training data, giving a 15% reduction in error rate over unsupervised approaches
Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell
This paper presents an approach for investigating the nature of semantic
information captured by word embeddings. We propose a method that extends an
existing human-elicited semantic property dataset with gold negative examples
using crowd judgments. Our experimental approach tests the ability of
supervised classifiers to identify semantic features in word embedding vectors
and com- pares this to a feature-identification method based on full vector
cosine similarity. The idea behind this method is that properties identified by
classifiers, but not through full vector comparison are captured by embeddings.
Properties that cannot be identified by either method are not. Our results
provide an initial indication that semantic properties relevant for the way
entities interact (e.g. dangerous) are captured, while perceptual information
(e.g. colors) is not represented. We conclude that, though preliminary, these
results show that our method is suitable for identifying which properties are
captured by embeddings.Comment: Accepted to the EMNLP workshop "Analyzing and interpreting neural
networks for NLP
On the automated extraction of regression knowledge from databases
The advent of inexpensive, powerful computing systems, together with the increasing amount of available data, conforms one of the greatest challenges for next-century information science. Since it is apparent that much future analysis will be done automatically, a good deal of attention has been paid recently to the implementation of ideas and/or the adaptation of systems originally developed in machine learning and other computer science areas. This interest seems to stem from both the suspicion that traditional techniques are not well-suited for large-scale automation and the success of new algorithmic concepts in difficult optimization problems. In this paper, I discuss a number of issues concerning the automated extraction of regression knowledge from databases. By regression knowledge is meant quantitative knowledge about the relationship between a vector of predictors or independent variables (x) and a scalar response or dependent variable (y). A number of difficulties found in some well-known tools are pointed out, and a flexible framework avoiding many such difficulties is described and advocated. Basic features of a new tool pursuing this direction are reviewed
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