23,264 research outputs found
A document management methodology based on similarity contents
The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain
A probabilistic framework for analysing the compositionality of conceptual combinations
Conceptual combination performs a fundamental role in creating the broad
range of compound phrases utilised in everyday language. This article provides
a novel probabilistic framework for assessing whether the semantics of conceptual
combinations are compositional, and so can be considered as a function of
the semantics of the constituent concepts, or not. While the systematicity and
productivity of language provide a strong argument in favor of assuming compositionality,
this very assumption is still regularly questioned in both cognitive
science and philosophy. Additionally, the principle of semantic compositionality
is underspecified, which means that notions of both "strong" and "weak"
compositionality appear in the literature. Rather than adjudicating between
different grades of compositionality, the framework presented here contributes
formal methods for determining a clear dividing line between compositional and
non-compositional semantics. In addition, we suggest that the distinction between
these is contextually sensitive. Compositionality is equated with a joint probability distribution modeling how the constituent concepts in the combination
are interpreted. Marginal selectivity is introduced as a pivotal probabilistic
constraint for the application of the Bell/CH and CHSH systems of inequalities.
Non-compositionality is equated with a failure of marginal selectivity, or violation
of either system of inequalities in the presence of marginal selectivity. This
means that the conceptual combination cannot be modeled in a joint probability
distribution, the variables of which correspond to how the constituent concepts
are being interpreted. The formal analysis methods are demonstrated by applying
them to an empirical illustration of twenty-four non-lexicalised conceptual
combinations
Reasoning about Independence in Probabilistic Models of Relational Data
We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related
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