Marking atomic events in sets of related texts

Filatova, Elena; Hatzivassiloglou, Vasileios

doi:10.1075/cilt.260.27fil

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…We use the next three techniques to achieve this: Named Entities: We use a named entities tagger to identify terms that give important clues about the topic of the document. Our choice is reinforced by existing research (e.g., [20], [21]) that shows that the use of named entities increases the quality of clustering and improves news event detection. We build on these ideas and use the named entities extracted from each news story as important terms that capture the important aspects of the document.…”

Section: Automatic Facet Discoverymentioning

confidence: 97%

Automatic Extraction of Useful Facet Hierarchies from Text Databases

Dakka

Ipeirotis

2008

2008 IEEE 24th International Conference on Data Engineering

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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.

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Section: Automatic Facet Discoverymentioning

confidence: 97%

Automatic Extraction of Useful Facet Hierarchies from Text Databases

Dakka

Ipeirotis

2008

2008 IEEE 24th International Conference on Data Engineering

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“…The first model of entity mention co-occurrence is based on an approach from the literature for identifying atomic events (Filatova and Hatzivassiloglou 2003). The first model of entity mention co-occurrence is based on an approach from the literature for identifying atomic events (Filatova and Hatzivassiloglou 2003).…”

Section: Systemsmentioning

confidence: 99%

Datasets for generic relation extraction

Hachey¹,

Grover²,

Tobin³

2011

Nat. Lang. Eng.

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A vast amount of usable electronic data is in the form of unstructured text. The relation extraction task aims to identify useful information in text (e.g. PersonW works for OrganisationX, GeneY encodes ProteinZ ) and recode it in a format such as a relational database or RDF triplestore that can be more effectively used for querying and automated reasoning. A number of resources have been developed for training and evaluating automatic systems for relation extraction in different domains. However, comparative evaluation is impeded by the fact that these corpora use different markup formats and notions of what constitutes a relation. We describe the preparation of corpora for comparative evaluation of relation extraction across domains based on the publicly available ACE 2004, ACE 2005 and BioInfer data sets. We present a common document type using token standoff and including detailed linguistic markup, while maintaining all information in the original annotation. The subsequent reannotation process normalises the two data sets so that they comply with a notion of relation that is intuitive, simple and informed by the semantic web. For the ACE data, we describe an automatic process that automatically converts many relations involving nested, nominal entity mentions to relations involving non-nested, named or pronominal entity mentions. For example, the first entity is mapped from 'one' to 'Amidu Berry' in the membership relation described in 'Amidu Berry, one half of PBS'. Moreover, we describe a comparably reannotated version of the BioInfer corpus that flattens nested relations, maps part-whole to part-part relations and maps n-ary to binary relations. Finally, we summarise experiments that compare approaches to generic relation extraction, a knowledge discovery task that uses minimally supervised techniques to achieve maximally portable extractors. These experiments illustrate the utility of the corpora. 1

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“…Our choice is reinforced by existing research (e.g., [107,129]) that shows that the use of named entities increases the quality of clustering and of news event detection. Our choice is reinforced by existing research (e.g., [107,129]) that shows that the use of named entities increases the quality of clustering and of news event detection.…”

Section: (D) = {Jacques Chirac 2005 G8 Summit}mentioning

confidence: 99%

Dynamic Taxonomies and Faceted Search

Sacco¹,

Tzitzikas²

2009

The Information Retrieval Series

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PrefaceCurrent search paradigms for the Web, direct access through search engines and navigational access via static taxonomies, have recently been strongly criticized. A third paradigm, dynamic taxonomies or faceted search, is gaining acceptance to the extent that it is now the de facto standard in product selection for e-commerce.This new paradigm is based on a simple and easily understood visual environment which supports both direct access and guided exploration of complex information bases. While focusing on structured, guided exploration, it also bridges the gap between traditional querying and browsing. In general, query services are either too simplistic (e.g. free text queries in IR systems or Web search engines), or too complex for casual users (e.g. SQL queries, or Semantic Web queries). Browsing as well, is either too simplistic (e.g. "plain" Web links) or application specific (dynamic pages derived by specific application programs), and does not support conceptual exploration.Dynamic taxonomies work on multidimensional taxonomies (usually organized by facets) and provide a single, coherent visual framework in which users can focus on one or more concepts in the taxonomy, and immediately see a conceptual summary of their focus, in the form of a reduced taxonomy derived from the original one by pruning unrelated concepts. Concepts in the reduced taxonomy can be used to set additional, dependent foci and users iterate in a guided yet unconstrained way until they reach a result set sufficiently small for manual inspection.The access paradigm supported is a conceptual exploration, far more frequent in "search" tasks than the retrieval by exact specification supported by search engines and database queries. The underlying model is simple and easily understood by users, offers substantial benefits over traditional approaches, and has an extremely wide application range, and a potential for important extensions. Dynamic taxonomy/faceted search is a heavily interdisciplinary area, where data modeling, human factors, logic, inference, and efficient implementations must be considered holistically.The goal of this book is to provide a complete and clear guide to all of the relevant aspects of dynamic taxonomies and faceted search. These include modeling, user interaction, taxonomy design, system implementation, and performance. The primary audience for this book are university students, professionals, and researchers in computer science and computer engineering who are interested in understanding and applying dynamic taxonomies, possibly in combination with other access methods, in real environments. The book may be of interest also to university students, professionals and researchers in Library and Information Science.The book is organized as follows. Chapter 1 introduces dynamic taxonomies and faceted search. Chapter 2 formally describes faceted taxonomy-based sources. In vii viii Preface Chap. 3, dynamic taxonomies and faceted search are compared to other techniques including information retrieval, OLAP, dy...

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Marking atomic events in sets of related texts

Cited by 5 publications

References 5 publications

Automatic Extraction of Useful Facet Hierarchies from Text Databases

Automatic Extraction of Useful Facet Hierarchies from Text Databases

Datasets for generic relation extraction

Dynamic Taxonomies and Faceted Search

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