3,017 research outputs found
Buzz monitoring in word space
This paper discusses the task of tracking mentions of some topically interesting textual entity from a continuously and dynamically changing flow of text, such as a news feed, the output from an Internet crawler or a similar text source - a task sometimes referred to as buzz monitoring. Standard approaches from the field of information access for identifying salient textual entities are reviewed, and it is argued that the dynamics of buzz monitoring calls for more accomplished analysis mechanisms than the typical text analysis tools provide today. The notion of word space is introduced, and it is argued that word spaces can be used to select the most salient markers for topicality, find associations those observations engender, and that they constitute an attractive foundation for building a representation well suited for the tracking and monitoring of mentions of the entity under consideration
Filaments of Meaning in Word Space
Word space models, in the sense of vector space models built on distributional data taken from texts, are used to model semantic relations between words. We argue that the high dimensionality of typical vector space models lead to unintuitive effects on modeling likeness of meaning and that the local structure of word spaces is where interesting semantic relations reside. We show that the local structure of word spaces has substantially different dimensionality and character than the global space and that this structure shows potential to be exploited for further semantic analysis using methods for local analysis of vector space structure rather than globally scoped methods typically in use today such as singular value decomposition or principal component analysis
Semantic Graph for Zero-Shot Learning
Zero-shot learning aims to classify visual objects without any training data
via knowledge transfer between seen and unseen classes. This is typically
achieved by exploring a semantic embedding space where the seen and unseen
classes can be related. Previous works differ in what embedding space is used
and how different classes and a test image can be related. In this paper, we
utilize the annotation-free semantic word space for the former and focus on
solving the latter issue of modeling relatedness. Specifically, in contrast to
previous work which ignores the semantic relationships between seen classes and
focus merely on those between seen and unseen classes, in this paper a novel
approach based on a semantic graph is proposed to represent the relationships
between all the seen and unseen class in a semantic word space. Based on this
semantic graph, we design a special absorbing Markov chain process, in which
each unseen class is viewed as an absorbing state. After incorporating one test
image into the semantic graph, the absorbing probabilities from the test data
to each unseen class can be effectively computed; and zero-shot classification
can be achieved by finding the class label with the highest absorbing
probability. The proposed model has a closed-form solution which is linear with
respect to the number of test images. We demonstrate the effectiveness and
computational efficiency of the proposed method over the state-of-the-arts on
the AwA (animals with attributes) dataset.Comment: 9 pages, 5 figure
The Word-Space Model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces
The word-space model is a computational model of word meaning that utilizes the distributional patterns of words collected over large text data to represent semantic similarity between words in terms of spatial proximity. The model has been used for over a decade, and has demonstrated its mettle in numerous experiments and applications. It is now on the verge of moving from research environments to practical deployment in commercial systems. Although extensively used and intensively investigated, our theoretical understanding of the word-space model remains unclear. The question this dissertation attempts to answer is: what kind of semantic information does the word-space model acquire and represent? The answer is derived through an identification and discussion of the three main theoretical cornerstones of the word-space model: the geometric metaphor of meaning, the distributional methodology, and the structuralist meaning theory. It is argued that the word-space model acquires and represents two different types of relations between words – syntagmatic and paradigmatic relations – depending on how the distributional patterns of words are used to accumulate word spaces. The difference between syntagmatic and paradigmatic word spaces is empirically demonstrated in a number of experiments, including comparisons with thesaurus entries, association norms, a synonym test, a list of antonym pairs, and a record of part-of-speech assignments.För att köpa boken skicka en beställning till [email protected]/ To order the book send an e-mail to [email protected]
Kristapurāṇa: Reshaping Divine Space
If a place is simply a physical location, the word space can be used for something shaped by mental processes. Physical places influence our lives by putting limits to the physically practicable, whereas spaces exercise their influence through mental processes like shaping our beliefs, values and sentiments. A space may be a mental superstructure based on an actual place, but, since its power is mental, it is not necessary that this place physically exists. One such space, with power to affect the lives of human beings, is heaven. Belief in heaven has had and still has great impact on many people’s thinking and acting. Heaven can be regarded as a part of a more general conceptual space inhabited by (ideas of) the divine and/or spiritual. I will refer to this as divine space
Word-Entity Duet Representations for Document Ranking
This paper presents a word-entity duet framework for utilizing knowledge
bases in ad-hoc retrieval. In this work, the query and documents are modeled by
word-based representations and entity-based representations. Ranking features
are generated by the interactions between the two representations,
incorporating information from the word space, the entity space, and the
cross-space connections through the knowledge graph. To handle the
uncertainties from the automatically constructed entity representations, an
attention-based ranking model AttR-Duet is developed. With back-propagation
from ranking labels, the model learns simultaneously how to demote noisy
entities and how to rank documents with the word-entity duet. Evaluation
results on TREC Web Track ad-hoc task demonstrate that all of the four-way
interactions in the duet are useful, the attention mechanism successfully
steers the model away from noisy entities, and together they significantly
outperform both word-based and entity-based learning to rank systems
Using Semantic Features Derived from Word-Space Models for Swedish Coreference Resolution
Proceedings of the 17th Nordic Conference of Computational Linguistics
NODALIDA 2009.
Editors: Kristiina Jokinen and Eckhard Bick.
NEALT Proceedings Series, Vol. 4 (2009), 134-141.
© 2009 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/9206
Discriminative Topological Features Reveal Biological Network Mechanisms
Recent genomic and bioinformatic advances have motivated the development of
numerous random network models purporting to describe graphs of biological,
technological, and sociological origin. The success of a model has been
evaluated by how well it reproduces a few key features of the real-world data,
such as degree distributions, mean geodesic lengths, and clustering
coefficients. Often pairs of models can reproduce these features with
indistinguishable fidelity despite being generated by vastly different
mechanisms. In such cases, these few target features are insufficient to
distinguish which of the different models best describes real world networks of
interest; moreover, it is not clear a priori that any of the presently-existing
algorithms for network generation offers a predictive description of the
networks inspiring them. To derive discriminative classifiers, we construct a
mapping from the set of all graphs to a high-dimensional (in principle
infinite-dimensional) ``word space.'' This map defines an input space for
classification schemes which allow us for the first time to state unambiguously
which models are most descriptive of the networks they purport to describe. Our
training sets include networks generated from 17 models either drawn from the
literature or introduced in this work, source code for which is freely
available. We anticipate that this new approach to network analysis will be of
broad impact to a number of communities.Comment: supplemental website:
http://www.columbia.edu/itc/applied/wiggins/netclass
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