Visualising the structure of document search results: A comparison of graph theoretic approaches

This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion

An approach to graph-based analysis of textual documents

In this paper a new graph-based model is proposed for the representation of textual documents. Graph-structures are obtained from textual documents by making use of the well-known Part-Of-Speech (POS) tagging technique. More specifically, a simple rule-based (re) classifier is used to map each tag onto graph vertices and edges. As a result, a decomposition of textual documents is obtained where tokens are automatically parsed and attached to either a vertex or an edge. It is shown how textual documents can be aggregated through their graph-structures and finally, it is shown how vertex-ranking methods can be used to find relevant tokens.(1)

Formulating representative features with respect to document genre classification

Genre classification (e.g. whether a document is a scientific article or magazine article) is closely bound to the physical and conceptual structure of document as well as the level of depth involved in the text. Hence, it provides a means of ranking documents retrieved by search tools according to metrics other than topical similarity. Moreover, the structural information derived from genre classification can be used to locate target information within the text. In previous studies, the detection of genre classes has been attempted by using some normalised frequency of terms or combinations of terms in the document (here, we are using term as a reference to words, phrases, syntactic units, sentences and paragraphs, as well as other patterns derived from deeper linguistic or semantic analysis). These approaches largely neglect how the term is distributed throughout the document. Here, we report the results of automated experiments based on distributive statistics of words in order to present evidence that term distribution pattern is a better indicator of genre class than term frequency.

Using Explicit Semantic Analysis for Cross-Lingual Link Discovery

This paper explores how to automatically generate cross language links between resources in large document collections. The paper presents new methods for Cross Lingual Link Discovery(CLLD) based on Explicit Semantic Analysis (ESA). The methods are applicable to any multilingual document collection. In this report, we present their comparative study on the Wikipedia corpus and provide new insights into the evaluation of link discovery systems. In particular, we measure the agreement of human annotators in linking articles in different language versions of Wikipedia, and compare it to the results achieved by the presented methods

A survey on the use of relevance feedback for information access systems

Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems

An analysis of the use of graphics for information retrieval

Several research groups have addressed the problem of retrieving vector graphics. This work has, however, focused either on domain-dependent areas or was based on very simple graphics languages. Here we take a fresh look at the issue of graphics retrieval in general and in particular at the tasks which retrieval systems must support. The paper presents a series of case studies which explored the needs of professionals in the hope that these needs can help direct future graphics IR research. Suggested modelling techniques for some of the graphic collections are also presented