21,553 research outputs found
Using distributional similarity to organise biomedical terminology
We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy
Large Margin Nearest Neighbor Embedding for Knowledge Representation
Traditional way of storing facts in triplets ({\it head\_entity, relation,
tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively
displayed and easily acquired by mankind, but hardly computed or even reasoned
by AI machines. Inspired by the success in applying {\it Distributed
Representations} to AI-related fields, recent studies expect to represent each
entity and relation with a unique low-dimensional embedding, which is different
from the symbolic and atomic framework of displaying knowledge in triplets. In
this way, the knowledge computing and reasoning can be essentially facilitated
by means of a simple {\it vector calculation}, i.e. . We thus contribute an effective model to learn better embeddings
satisfying the formula by pulling the positive tail entities to
get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and
simultaneously pushing the negatives away from the positives
via keeping a {\it Large Margin}. We also design a corresponding
learning algorithm to efficiently find the optimal solution based on {\it
Stochastic Gradient Descent} in iterative fashion. Quantitative experiments
illustrate that our approach can achieve the state-of-the-art performance,
compared with several latest methods on some benchmark datasets for two
classical applications, i.e. {\it Link prediction} and {\it Triplet
classification}. Moreover, we analyze the parameter complexities among all the
evaluated models, and analytical results indicate that our model needs fewer
computational resources on outperforming the other methods.Comment: arXiv admin note: text overlap with arXiv:1503.0815
Discrete Elastic Inner Vector Spaces with Application in Time Series and Sequence Mining
This paper proposes a framework dedicated to the construction of what we call
discrete elastic inner product allowing one to embed sets of non-uniformly
sampled multivariate time series or sequences of varying lengths into inner
product space structures. This framework is based on a recursive definition
that covers the case of multiple embedded time elastic dimensions. We prove
that such inner products exist in our general framework and show how a simple
instance of this inner product class operates on some prospective applications,
while generalizing the Euclidean inner product. Classification experimentations
on time series and symbolic sequences datasets demonstrate the benefits that we
can expect by embedding time series or sequences into elastic inner spaces
rather than into classical Euclidean spaces. These experiments show good
accuracy when compared to the euclidean distance or even dynamic programming
algorithms while maintaining a linear algorithmic complexity at exploitation
stage, although a quadratic indexing phase beforehand is required.Comment: arXiv admin note: substantial text overlap with arXiv:1101.431
Analyzing analytical methods: The case of phonology in neural models of spoken language
Given the fast development of analysis techniques for NLP and speech
processing systems, few systematic studies have been conducted to compare the
strengths and weaknesses of each method. As a step in this direction we study
the case of representations of phonology in neural network models of spoken
language. We use two commonly applied analytical techniques, diagnostic
classifiers and representational similarity analysis, to quantify to what
extent neural activation patterns encode phonemes and phoneme sequences. We
manipulate two factors that can affect the outcome of analysis. First, we
investigate the role of learning by comparing neural activations extracted from
trained versus randomly-initialized models. Second, we examine the temporal
scope of the activations by probing both local activations corresponding to a
few milliseconds of the speech signal, and global activations pooled over the
whole utterance. We conclude that reporting analysis results with randomly
initialized models is crucial, and that global-scope methods tend to yield more
consistent results and we recommend their use as a complement to local-scope
diagnostic methods.Comment: ACL 202
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