A RE-UNIFICATION OF TWO COMPETING MODELS FOR DOCUMENT RETRIEVAL

Two competing approaches for document retrieval were first identified by Robertson et al (Robertson, Maron et al. 1982) for probabilistic retrieval. We point out the corresponding two competing approaches for the Vector Space Model. In both the probabilistic and Vector Space models, only one of the two competing approaches has received significant research attention, because of the unavailibility of sufficient data to implement the second approach. Because it is now feasible to collect vast amounts of feedback data from users, both approaches are now possible. We therefore re-visit the question of a unification of both approaches, for both probabilistic and Vector Space models. This unification of approaches differs from that originally proposed in (Robertson, Maron et al. 1982), and offers unique advantages. Preliminary results of a simulation experiment are reported, and an outline is provided of an ongoing field study.Information Systems Working Papers Serie

A RE-UNIFICATION OF TWO COMPETING MODELS FOR DOCUMENT RETRIEVAL

Two competing approaches for document retrieval were first identified by Robertson et al (Robertson, Maron et al. 1982) for probabilistic retrieval. We point out the corresponding two competing approaches for the Vector Space Model. In both the probabilistic and Vector Space models, only one of the two competing approaches has received significant research attention, because of the unavailibility of sufficient data to implement the second approach. Because it is now feasible to collect vast amounts of feedback data from users, both approaches are now possible. We therefore re-visit the question of a unification of both approaches, for both probabilistic and Vector Space models. This unification of approaches differs from that originally proposed in (Robertson, Maron et al. 1982), and offers unique advantages. Preliminary results of a simulation experiment are reported, and an outline is provided of an ongoing field study.Information Systems Working Papers Serie

Neural Mechanisms for Information Compression by Multiple Alignment, Unification and Search

This article describes how an abstract framework for perception and cognition may be realised in terms of neural mechanisms and neural processing. This framework — called information compression by multiple alignment, unification and search (ICMAUS) — has been developed in previous research as a generalized model of any system for processing information, either natural or artificial. It has a range of applications including the analysis and production of natural language, unsupervised inductive learning, recognition of objects and patterns, probabilistic reasoning, and others. The proposals in this article may be seen as an extension and development of Hebb’s (1949) concept of a ‘cell assembly’. The article describes how the concept of ‘pattern’ in the ICMAUS framework may be mapped onto a version of the cell assembly concept and the way in which neural mechanisms may achieve the effect of ‘multiple alignment’ in the ICMAUS framework. By contrast with the Hebbian concept of a cell assembly, it is proposed here that any one neuron can belong in one assembly and only one assembly. A key feature of present proposals, which is not part of the Hebbian concept, is that any cell assembly may contain ‘references’ or ‘codes’ that serve to identify one or more other cell assemblies. This mechanism allows information to be stored in a compressed form, it provides a robust mechanism by which assemblies may be connected to form hierarchies and other kinds of structure, it means that assemblies can express abstract concepts, and it provides solutions to some of the other problems associated with cell assemblies. Drawing on insights derived from the ICMAUS framework, the article also describes how learning may be achieved with neural mechanisms. This concept of learning is significantly different from the Hebbian concept and appears to provide a better account of what we know about human learning

Software Infrastructure for Natural Language Processing

We classify and review current approaches to software infrastructure for research, development and delivery of NLP systems. The task is motivated by a discussion of current trends in the field of NLP and Language Engineering. We describe a system called GATE (a General Architecture for Text Engineering) that provides a software infrastructure on top of which heterogeneous NLP processing modules may be evaluated and refined individually, or may be combined into larger application systems. GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE promotes reuse of component technology, permits specialisation and collaboration in large-scale projects, and allows for the comparison and evaluation of alternative technologies. The first release of GATE is now available - see http://www.dcs.shef.ac.uk/research/groups/nlp/gate/Comment: LaTeX, uses aclap.sty, 8 page