Automatic Extraction of Useful Facet Hierarchies from Text Databases

Abstract

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with "context" phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.

A Survey on Mining Aspects for Queries

“…Most existing faceted search and facets generation systems are built on a specific domain such as product search or predefined facet categories. For example, Dakka and Ipeirotis [2] introduced an unsupervised technique for automatic extraction of facets that are useful for browsing text databases. Facet hierarchies are generated for a whole collection, instead of for a given query.Facets of a query are automatically mined from the top web search results of the query without any additional domain knowledge required.…”

Exploratory Web Searching with Dynamic Taxonomies and Results Clustering

“…An example of the idea assuming only one facet, is shown in Figure 1. Figure 1(a) shows a taxonomy and 8 indexed objects (1)(2)(3)(4)(5)(6)(7)(8). The user explores or navigates the information space by setting and changing his focus.…”

“…Clustering the snippets rather than the whole documents makes clustering algorithms faster. Some clustering algorithms [6,5,23] use internal or external sources of knowledge like Web directories (e.g. DMoz 3 ), Web dictionaries (e.g.…”

STC+ and NM-STC: Two Novel Online Results Clustering Methods for Web Searching

“…It extracts salient phrases as candidate cluster names from the list of titles and snippets of the answer, and ranks them using a regression model over five different properties, learned from human training data. Another approach that uses several external resources, such as WordNet and Wikipedia, in order to identify useful terms and to organize them hierarchically is described in [4].…”