Laurianne Sitbon

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Peter D. Bruza63
Thomas K. Landauer44
W. Bruce Croft39
Susan T. Dumais35
Guido Zuccon30
45Peter D. Bruza
39Bevan Raymond Koopman
30Guido Zuccon
19Anthony N. Nguyen
18Patrice Bellot

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Medical Semantic Similarity with a Neural Language Model
Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text. This model is employed… (More)
An evaluation of corpus-driven measures of medical concept similarity for information retrieval
Measures of semantic similarity between medical concepts are central to a number of techniques in medical informatics, including query expansion in medical information retrieval. Previous work has mainly considered thesaurus-based path measures of semantic similarity and has not compared different corpus-driven approaches in depth. We evaluate the… (More)
AML: Efficient Approximate Membership Localization within a Web-Based Join Framework
In this paper, we propose a new type of Dictionary-based Entity Recognition Problem, named Approximate Membership Localization (AML). The popular Approximate Membership Extraction (AME) provides a full coverage to the true matched substrings from a given document, but many redundancies cause a low efficiency of the AME process and deteriorate the… (More)
A probabilistic framework for analysing the compositionality of conceptual combinations
Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilized 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.… (More)
Quantum-like non-separability of concept combinations, emergent associates and abduction
Consider the concept combination “pet human”. In word association experiments, human subjects produce the associate “slave” in relation this combination. The striking aspect of this associate is that it is not produced as an associate of “pet”, or “human” in isolation. In other words, the associate “slave” seems to be emergent. Such emergent associations… (More)
Information retrieval as semantic inference: a Graph Inference model applied to medical search
This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is… (More)
A web-based approach to data imputation
In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating… (More)
Analysis of the effect of negation on information retrieval of medical data
Most information retrieval (IR) models treat the presence of a term within a document as an indication that the document is somehow “about” that term, they do not take into account when a term might be explicitly negated. Medical data, by its nature, contains a high frequency of negated terms – e.g. “review of systems showed no chest pain or shortness of… (More)
Modelling Word Meaning using Efficient Tensor Representations
Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric representations of words to store semantic associations between words. Often word order information is not captured in these models. The lack of structural information used by these… (More)