First Story Detection using a Composite Document Representation

First Story Detection using a Composite Document Representation. Nicola Stokes, Joe Carthy, Department of Computer Science, University College Dublin, Ireland. {nicola.stokes,joe.carthy}@ucd.ie ABSTRACT In this paper, we explore the effects of data fusion on First Story Detection [1] in a broadcast news domain. The data fusion element of this experiment involves the combination of evidence derived from two distinct representations of document content in a single cluster run. Our composite document representation consists of a concept representation (based on the lexical chains derived from a text) and free text representation (using traditional keyword index terms). Using the TDT1 evaluation methodology we evaluate a number of document representation strategies and propose reasons why our data fusion experiment shows performance improvements in the TDT domain. Keywords Lexical Chaining, Data Fusion, First Story Detection. 1. INTRODUCTION The goal of TDT is to monitor and reorganize a stream of broadcast news stories in such a way as to help a user recognize and explore different news events that have occurred in the data set. First story detection (or online new event detection [1]) is one aspect of the detection problem which constitutes one of the three technical tasks defined by the TDT initiative (the other two being segmentation and tracking). Given a stream of news stories arriving in chronological order, a detection system must group or cluster articles that discuss distinct news events in the data stream. The TDT initiative has further clarified the notion of topic detection by differentiating between classification in a retrospective (Event Clustering) and an online environment (First Story Detection). In FSD the system must identify all stories in the data stream that discuss novel news events. This classification decision is made by considering only those documents that have arrived prior to the current document being evaluated, forcing the system to adhere to the temporal constraints of a real-time news stream. In other words the system must make an irrevocable classification decision (i.e. either the document discusses a new event or previously detected event) as soon as the document arrives on the input stream. The goal of event clustering on the other hand is to partition the data stream into clusters of related documents that discuss distinct events. This decision can be made after the system has considered all the stories in the input stream. In addition to defining three research problems associated with broadcast news, the TDT initiative also attempted to formally define an event with respect to how it differs from the traditional IR notion of a subject or a topic as defined by the TREC community. An event is defined as ‘something that happens at some specific time and place (e.g. an assassination attempt, or a volcanic eruption in Greece)’. A topic on the other hand is a ‘seminal event or activity along with all directly related events and activities (e.g. an investigation or a political campaign)’ [1]. Initial TDT research into event tracking and detection focused on developing a classification algorithm to address this subtle distinction between …

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