1,744 research outputs found
Human-Level Performance on Word Analogy Questions by Latent Relational Analysis
This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus
The SEC-system : reuse support for scheduling system development
Recently, in a joint cooperation of Stichting VNA, SAL Apotheken, the Faculty of Management and Organization, and the University Centre for Pharmacy, University of Groningen in the Netherlands, a Ph.D-study started regarding Apot(he)ek, Organization and Management (APOM). The APOM-project deals with the structuring and steering of pharmacy organization. The manageability of the internal pharmacy organization, and the manageability of the direct environment of pharmacy organization is the subject matter. The theoretical background of the APOM-project is described. A literature study was made to find mixes of objectives. Three mixes of objectives in pharmacy organization are postulated; the product mix, the process mix, and the customer mix. The typology will be used as a basic starting point for the empirical study in the next phase of the APOM-project.
Similarity of Semantic Relations
There are at least two kinds of similarity. Relational similarity is
correspondence between relations, in contrast with attributional similarity,
which is correspondence between attributes. When two words have a high
degree of attributional similarity, we call them synonyms. When two pairs
of words have a high degree of relational similarity, we say that their
relations are analogous. For example, the word pair mason:stone is analogous
to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA),
a method for measuring relational similarity. LRA has potential applications in many
areas, including information extraction, word sense disambiguation,
and information retrieval. Recently the Vector Space Model (VSM) of information
retrieval has been adapted to measuring relational similarity,
achieving a score of 47% on a collection of 374 college-level multiple-choice
word analogy questions. In the VSM approach, the relation between a pair of words is
characterized by a vector of frequencies of predefined patterns in a large corpus.
LRA extends the VSM approach in three ways: (1) the patterns are derived automatically
from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency
data, and (3) automatically generated synonyms are used to explore variations of the
word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the
average human score of 57%. On the related problem of classifying semantic relations, LRA
achieves similar gains over the VSM
The dictionaries in which we learn to think
Taking its title from the discussion of a ânew Menoâ to be found in Difference and Repetition, through an examination of the link between learning and thinking set out across Deleuze\u27s work this paper charts the important sense in which philosophical thought is characterised by an apprenticeship. The claim is that just as certain aesthetic and biological processes involve inscrutable and non-resembling elements that cannot be known in advance, the experience of learning is one oriented by unforseen encounters. With a view to a peculiarly heuristic use of dictionaries in the case of language learning, the paper shows how the logic (or event) of this experience is one whereby the putative meaning of things does not enjoy a priority over the immanence of their expression
Deterministic walks in random networks: an application to thesaurus graphs
In a landscape composed of N randomly distributed sites in Euclidean space, a
walker (``tourist'') goes to the nearest one that has not been visited in the
last \tau steps. This procedure leads to trajectories composed of a transient
part and a final cyclic attractor of period p. The tourist walk presents
universal aspects with respect to \tau and can be done in a wide range of
networks that can be viewed as ordinal neighborhood graphs. As an example, we
show that graphs defined by thesaurus dictionaries share some of the
statistical properties of low dimensional (d=2) Euclidean graphs and are easily
distinguished from random graphs. This approach furnishes complementary
information to the usual clustering coefficient and mean minimum separation
length.Comment: 12 pages, 5 figures, revised version submited to Physica A, corrected
references to figure
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Inventing Requirements from Software: An Empirical Investigation with Web Services
Service-centric software systems offer new opportunities for requirements processes. This paper reports a new tool designed to increase the completeness of system requirements using information about designs and implementations of web services. It presents an algorithm for retrieving web services in domains that are analogical to a current requirements problem, to support creative thinking about requirements for that problem. It describes how the algorithm parses and analogically matches natural language descriptions of system requirements and web service descriptions. The paper also reports 2 evaluations of the tool that demonstrate improvements to specifications of requirements for a system in the automotive domain
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A Computational Model of Analogical Reasoning in Dementia Care
This paper reports a practical application of a computational model of analogical reasoning to a pressing social problem, which is to improve the care of older people with dementia. Underpinning the support for carers for people with dementia is a computational model of analogical reasoning that retrieves information about cases from analogical problem domains. The model implements structure-mapping theory adapted to match source and target domains expressed in unstructured natural language. The model is implemented as a computational service invoked by a mobile app used by carers during their care shifts
Automated Retrieval of Non-Engineering Domain Solutions to Engineering Problems
Organised by: Cranfield UniversityBiological inspiration for engineering design has occurred through a variety of techniques such as creation
and use of databases, keyword searches of biological information in natural-language format, prior
knowledge of biology, and chance observations of nature. This research focuses on utilizing the reconciled
Functional Basis function and flow terms to identify suitable biological inspiration for function based design.
The organized search provides two levels of results: (1) associated with verb function only and (2) narrowed
results associated with verb-noun (function-flow). A set of heuristics has been complied to promote efficient
searching using this technique. An example for creating smart flooring is also presented and discussed.Mori Seiki â The Machine Tool Compan
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