Comparing Effectiveness in TDT and IR

Allan, James; Lavrenko, Victor; Jin, Hubert

doi:10.21236/ada477533

“…It was argued by Allan et al [4] that there were different similarity measures, ranging from cosine distance measure to distributional language models. An example of similarity measure was given in which agglomerative clustering methods were extensively explored to determine if a news story is describing a new event or a redundant one as compared to the previously identified documents.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There is a large volume of published studies utilising TDT test data for evaluation, in accordance with [4]. The TDT benchmark collection is poorly annotated: TDT5 covers 278,109 English news events but only 100 themes and approximately 4,500 labelled documents.…”

Section: Benchmark Datasetmentioning

confidence: 99%

An Improved System for Sentence-level Novelty Detection in Textual Streams

Fu

¹

,

Ch’ng

²

,

Aickelin

³

et al. 2015

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Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new terms requires adaptation in the vector space model. We present a novel event detection system based on the Incremental Term Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with Locality Sensitive Hashing (LSH). Our system could efficiently and effectively adapt to the changes within the data streams of any new terms with continual updates to the vector space model. Regarding miss probability, our proposed novelty detection framework outperforms a recognised baseline system by approximately 16% when evaluating a benchmark dataset from Google News.

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“…Each new document is then compared to the previous ones, and if it has similarity to the closest document (or centroid) below a certain threshold, the new document is declared as a First Story or new event. This approach is used in the UMass and the CMU system [4].…”

Section: Vector Space Modelmentioning

confidence: 99%

An Improved System For Sentence-Level Novelty Detection In Textual Streams

Fu¹,

Ch’ng²,

Aickelin³

et al. 2015

2015 International Conference on Smart and Sustainable City and Big Data (ICSSC)

1

0

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Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new terms requires adaptation in the vector space model. We present a novel event detection system based on the Incremental Term Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with Locality Sensitive Hashing (LSH). Our system could efficiently and effectively adapt to the changes within the data streams of any new terms with continual updates to the vector space model. Regarding miss probability, our proposed novelty detection framework outperforms a recognised baseline system by approximately 16% when evaluating a benchmark dataset from Google News.

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“…Most approaches to the story link detection task relied on text similarity. For example the cosine similarity and the clarity metric have been found to be very effective [3,16]. In addition, many approaches focused on matching named entities.…”

Section: Related Workmentioning

confidence: 99%

Folktale Classification Using Learning to Rank

Nguyen

¹

,

Trieschnigg

²

,

Theune

³

2013

Lecture Notes in Computer Science

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Abstract. We present a learning to rank approach to classify folktales, such as fairy tales and urban legends, according to their story type, a concept that is widely used by folktale researchers to organize and classify folktales. A story type represents a collection of similar stories often with recurring plot and themes. Our work is guided by two frequently used story type classification schemes. Contrary to most information retrieval problems, the text similarity in this problem goes beyond topical similarity. We experiment with approaches inspired by distributed information retrieval and features that compare subject-verb-object triplets. Our system was found to be highly effective compared with a baseline system.

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Comparing Effectiveness in TDT and IR

Cited by 10 publications

References 0 publications

An Improved System for Sentence-level Novelty Detection in Textual Streams

An Improved System for Sentence-level Novelty Detection in Textual Streams

An Improved System For Sentence-Level Novelty Detection In Textual Streams

Folktale Classification Using Learning to Rank

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