Hierarchical Orderings of Textual Units

Hierarchical Orderings of Textual Units Alexander Mehler University of Trier Universit¨atsring 15 D-54286 Trier, Germany mehler@uni-trier.de Abstract Text representation is a central task for any ap- proach to automatic learning from texts. It re- quires a format which allows to interrelate texts even if they do not share content words, but deal with similar topics. Furthermore, measur- ing text similarities raises the question of how to organize the resulting clusters. This paper presents cohesion trees (CT) as a data structure for the perspective, hierarchical organization of text corpora. CTs operate on alternative text representation models taking lexical organiza- tion, quantitative text characteristics, and text structure into account. It is shown that CTs realize text linkages which are lexically more homogeneous than those produced by minimal spanning trees. 1 Introduction Text representation is a central task for ap- proaches to text classification or categorization. They require a format which allows to seman- tically relate words, texts, and thematic cate- gories. The majority of approaches to automa- tic learning from texts use the vector space or bag of words model. Although there is much re- search for alternative formats, whether phrase- or hyperonym-based, their effects seem to be small (Scott and Matwin, 1999). More serious- ly (Riloff, 1995) argues that the bag of words model ignores morphological and syntactical in- formation which she found to be essential for solving some categorization tasks. An alterna- tive to the vector space model are semantic spaces, which have been proposed as a high- dimensional format for representing relations of semantic proximity. Relying on sparse know- ledge resources, they prove to be efficient in cog- nitive science (Kintsch, 1998; Landauer and Du- mais, 1997), computational linguistics (Rieger, 1984; Sch¨utze, 1998), and information retrieval. Although semantic spaces prove to be an al- ternative to the vector space model, they leave the question unanswered of how to explore and visualize similarities of signs mapped onto them. In case that texts are represented as points in semantic space, this question refers to the ex- ploration of their implicit, content based rela- tions. Several methods for solving this task have been proposed which range from simple lists via minimal spanning trees to cluster analysis as part of scatter/gahter algorithms (Hearst and Pedersen, 1996). Representing a sign’s environ- ment in space by means of lists runs the risk of successively ordering semantically or themati- cally diverse units. Obviously, lists neglect the poly-hierarchical structure of semantic spaces which may induce divergent thematic progres- sions starting from the same polysemous unit. Although clustering proves to be an alternative to lists, it seeks a global, possibly nested par- tition in which clusters represent sets of indis- tinguishable objects regarding the cluster crite- rion. In contrast to this, we present cohesion trees (CT) as a data structure, in which single objects are hierarchically ordered on the basis of lexical cohesion. CTs, whose field of application is the management of search results in IR, shift the perspective from sets of clustered objects to cohesive paths of …

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