Seher Acer, Başak Çakar, Elif Demirli, Şadiye Kaptanoğlu.
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Transcript of Seher Acer, Başak Çakar, Elif Demirli, Şadiye Kaptanoğlu.
![Page 1: Seher Acer, Başak Çakar, Elif Demirli, Şadiye Kaptanoğlu.](https://reader033.fdocuments.net/reader033/viewer/2022061513/5516f79b550346fe558b4cd3/html5/thumbnails/1.jpg)
Seher Acer, Başak Çakar, Elif Demirli, Şadiye Kaptanoğlu
![Page 2: Seher Acer, Başak Çakar, Elif Demirli, Şadiye Kaptanoğlu.](https://reader033.fdocuments.net/reader033/viewer/2022061513/5516f79b550346fe558b4cd3/html5/thumbnails/2.jpg)
Introduction Motivation Aspect Based Clustering
◦ Modeling Aspects◦ Aspect Extraction◦ Framing Cycle-Aware Clustering
User Interface & Demo Conclusion References
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News are produced in multiple stages:◦ Gathering, writing, editing, etc.
Subjective opinion of producers, owners, advertisers – biased environment
Effort needed for a comprehensive and balanced understanding of a news event
A system that guides and encourages reader to read news from different perspectives
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Current systems provide limited presentation of news◦ Listing news arbitrarily or according to date
A system that helps users reach news from different viewpoints via a single portal
Capture the difference of aspects within articles reporting a common news story
Use of advanced computational techniques of information retrieval
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Aspect: keyword-weight pairs Keywords are extracted from
◦ Head, sub-head, lead GATE (General Architecture for Text
Engineering)◦ Person, organization, location
Event extraction (Zemberek)◦ Frequently used action words/phrases
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Set of articles on a news shows head-tail characteristics
Head – common aspects Tail – uncommon aspects Separation of head and tail provides
effective classification Two steps:
◦ Head-tail partitioning◦ Tail-side clustering
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Generate common-uncommon keyword sets HgP: head group proportion Calculate keyword commonness &
uncommonness Commonness – an article with many
common keywords with high weight values Uncommonness - an article with many
uncommon keywords with high weight values
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Agglomerative hierarchical clustering Similarity measure – Cosine similarity During Agglomerative Clustering
◦ Each object forms a cluster of its own as a singleton
◦ Pairs of clusters are merged iteratively until a certain stopping criterion is met
◦ In the merging process - the similarity between two clusters is measured by the similarity of the most similar pair of sequences belonging to these two clusters (the single-link approach)
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Simple & user-friendly Present news from different aspects fairly Motivate reader to read news from different
aspects
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Existing systems: Google news, Yahoo News◦ Limited presentation◦ News listed arbitrarily
Proposed system:◦ Gathers same news with existing systems◦ Clusters news according to aspects◦ Simple user interface◦ Easy to track news stories
The approach is suitable for Turkish news
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[1] Park, S., Kang, S., Lee, S., Chung, S., Song, J. Mitigating Media Bias: A Computational Approach. ACM, 2008, pp. 47-51.
[2] Park, S., Kang, S., Chung, S., Song, J. NewsCube: Delivering Multiple Aspects of News to Mitigate Media Bias. ACM, 2009.
[3] Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics. ACL'02, 2002.
[4] Park, S., Lee, S., Song, J. Aspect-level News Browsing: Understanding News Events from Multiple Viewpoints. ACM, 2010, pp. 41-50.
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