14,678 research outputs found
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Link Prediction in Complex Networks: A Survey
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure
Normalized Web Distance and Word Similarity
There is a great deal of work in cognitive psychology, linguistics, and
computer science, about using word (or phrase) frequencies in context in text
corpora to develop measures for word similarity or word association, going back
to at least the 1960s. The goal of this chapter is to introduce the
normalizedis a general way to tap the amorphous low-grade knowledge available
for free on the Internet, typed in by local users aiming at personal
gratification of diverse objectives, and yet globally achieving what is
effectively the largest semantic electronic database in the world. Moreover,
this database is available for all by using any search engine that can return
aggregate page-count estimates for a large range of search-queries. In the
paper introducing the NWD it was called `normalized Google distance (NGD),' but
since Google doesn't allow computer searches anymore, we opt for the more
neutral and descriptive NWD. web distance (NWD) method to determine similarity
between words and phrases. ItComment: Latex, 20 pages, 7 figures, to appear in: Handbook of Natural
Language Processing, Second Edition, Nitin Indurkhya and Fred J. Damerau
Eds., CRC Press, Taylor and Francis Group, Boca Raton, FL, 2010, ISBN
978-142008592
Learning Word Representations from Relational Graphs
Attributes of words and relations between two words are central to numerous
tasks in Artificial Intelligence such as knowledge representation, similarity
measurement, and analogy detection. Often when two words share one or more
attributes in common, they are connected by some semantic relations. On the
other hand, if there are numerous semantic relations between two words, we can
expect some of the attributes of one of the words to be inherited by the other.
Motivated by this close connection between attributes and relations, given a
relational graph in which words are inter- connected via numerous semantic
relations, we propose a method to learn a latent representation for the
individual words. The proposed method considers not only the co-occurrences of
words as done by existing approaches for word representation learning, but also
the semantic relations in which two words co-occur. To evaluate the accuracy of
the word representations learnt using the proposed method, we use the learnt
word representations to solve semantic word analogy problems. Our experimental
results show that it is possible to learn better word representations by using
semantic semantics between words.Comment: AAAI 201
Leveraging Node Attributes for Incomplete Relational Data
Relational data are usually highly incomplete in practice, which inspires us
to leverage side information to improve the performance of community detection
and link prediction. This paper presents a Bayesian probabilistic approach that
incorporates various kinds of node attributes encoded in binary form in
relational models with Poisson likelihood. Our method works flexibly with both
directed and undirected relational networks. The inference can be done by
efficient Gibbs sampling which leverages sparsity of both networks and node
attributes. Extensive experiments show that our models achieve the
state-of-the-art link prediction results, especially with highly incomplete
relational data.Comment: Appearing in ICML 201
Aversive parenting in China: Associations with child physical and relational aggression
This study assessed the combined and differential contributions of Chinese mothers and fathers (in terms of spouse-reported physically coercive and psychologically controlling parenting) to the development of peerreported physical and relational aggression in their preschool-age children (mean age of 5 years). Results of the two-group (boys and girls) latent sum and difference structural equation model showed that combined parenting effects were slightly more prevalent than differential effects in predicting aggression. Furthermore, physical coercion was predictive of aggression in boys whereas psychological control was primarily associated with aggression in girls. Findings extend our understanding of relational aggression and the meaning of aversive parenting, particularly within the Chinese cultural context. © 2006 by the Society for Research in Child Development Inc. All rights reserved
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Entity alignment is the task of finding entities in two knowledge bases (KBs)
that represent the same real-world object. When facing KBs in different natural
languages, conventional cross-lingual entity alignment methods rely on machine
translation to eliminate the language barriers. These approaches often suffer
from the uneven quality of translations between languages. While recent
embedding-based techniques encode entities and relationships in KBs and do not
need machine translation for cross-lingual entity alignment, a significant
number of attributes remain largely unexplored. In this paper, we propose a
joint attribute-preserving embedding model for cross-lingual entity alignment.
It jointly embeds the structures of two KBs into a unified vector space and
further refines it by leveraging attribute correlations in the KBs. Our
experimental results on real-world datasets show that this approach
significantly outperforms the state-of-the-art embedding approaches for
cross-lingual entity alignment and could be complemented with methods based on
machine translation
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