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SNOW 2014 Data Challenge
 

SNOW 2014 Data Challenge

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Overview presentation of SNOW 2014 Data Challenge including results.

Overview presentation of SNOW 2014 Data Challenge including results.

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    SNOW 2014 Data Challenge SNOW 2014 Data Challenge Presentation Transcript

    • WWW 2014 Seoul, April 8th SNOW 2014 Data Challenge Symeon Papadopoulos (CERTH) David Corney (RGU) Luca Aiello (Yahoo! Labs)
    • Overview of Challenge • Goal: Detection of newsworthy topics in a large and noisy set of tweets • Topic: a news story represented by a headline + tags + representative tweets + representative images (optional) • Newsworthy: A topic that ends up being covered by at least some major online news sources • Topics are detected per timeslot (small equally-sized time intervals) • We want a maximum number of topics per timeslot #2
    • Challenge Activity Log • Challenge definition (Dec 2013) • Challenge toolkit and registration (Jan 20, 2014) • Development dataset collection (Feb 3, 2014) • Rehearsal dataset collection (Feb 17, 2014) • Test dataset collection (Feb 25, 2014) • Results submission (Mar 4, 2014) • Paper submission (Mar 9, 2014) • Results evaluation (Mar 5-18, 2014) • Workshop (Apr 7, 2014) #3
    • Some statistics • Registered participants: 25 – India: 4, Belgium: 3, Germany: 3, UK: 3, Greece: 3, Ireland: 2, USA: 2, France: 2, Italy: 1, Spain: 1, Russia: 1 • Participants that signed the Challenge agreement: 19 • Participants that submitted results: 11 • Participants that submitted papers: 9 #4
    • Evaluation Protocol • Defined several evaluation criteria: – Newsworthiness  Precision/Recall, F-score – Readability  scale [1-5] – Coherence  scale [1-5] – Diversity  scale [1-5] • List of reference topics • Set up precise evaluation guidelines • Blind evaluation (i.e. evaluator not aware of which method a topic comes from) based on Web UI • Participants submitted topics for 96 timeslots, but manual evaluation happened for 5 sample timeslots. • Result validation and analysis #5
    • Teams key #6 Key Team A UKON B IBCN C ITI D math-dyn E Insight F FUB-TORV G PILOTS H RGU I UoGMIR J EURECOM K SNOWBITS References to the submitted papers will be included in the overview paper in the workshop proceedings.
    • Results – Reference topic recall #7 Team Recall (%) Rank A 0.44 5 B 0.58 4 C 0.32 7 D 0.63 2 E 0.66 1 F 0.39 6 G 0.24 8 H 0.6 3 I 0.17 10 J 0.24 8 K 0.14 11 Recall computed with respect to 59 reference topics. Those were partitioned in three groups (20, 20, 19) and each of the three evaluators manually matched the topics of participants to the topics assigned to him. Eval. Pair Correlation Eval. 1 – Eval. 2 0.894913 Eval. 1 – Eval. 3 0.930247 Eval. 2 – Eval. 3 0.811976
    • Results – Pooled topic recall (1/2) • Each evaluator independently evaluated the topics of each participant as newsworthy or not • Selected all topics that were marked as newsworthy by at least two evaluators • Manually extracted the unique topics (70 in total, partially overlapping with reference topic list) • Manually matched correct topics of each participants to the list of newsworthy topics • Computed precision, recall and F-score #8
    • Results – Pooled topic recall (2/2) #9 Team Matched Unique Total Prec Rec F-score Rank A 13 13 27 0.481 0.186 0.268 6 B 12 12 23 0.522 0.171 0.258 7 C 22 15 50 0.44 0.214 0.288 4 D 18 14 39 0.462 0.2 0.279 5 E 28 25 50 0.56 0.357 0.436 1 F 4 2 15 0.267 0.029 0.052 10 G 4 4 10 0.4 0.057 0.099 9 H 19 17 49 0.388 0.243 0.299 3 I 36 15 45 0.8 0.214 0.338 2 J 1 1 8 0.125 0.014 0.027 11 K 8 7 10 0.8 0.1 0.178 8
    • Results - Readability #10 Team Readability Rank A 4.29 9 B 4.92 2 C 4.49 7 D 4.59 6 E 4.74 4 F 4.18 10 G 4.93 1 H 4.71 5 I 4.8 3 J 3.38 11 K 4.32 8 Eval. Pair Correlation Eval. 1 – Eval. 2 0.902124 Eval. 1 – Eval. 3 0.357733 Eval. 2 – Eval. 3 0.278632
    • Results - Coherence #11 Team Coherence Rank A 4.4 6 B 4.08 9 C 4.68 5 D 4.91 2 E 4.97 1 F 4.78 4 G 4.83 3 H 4.22 8 I 3.95 10 J 3.75 11 K 4.36 7 Eval. Pair Correlation Eval. 1 – Eval. 2 0.549512 Eval. 1 – Eval. 3 0.730684 Eval. 2 – Eval. 3 0.684426
    • Results - Diversity #12 Team Diversity Rank A 2.12 7 B 2.36 4 C 2.31 6 D 2.11 8 E 2.11 8 F 2 10 G 1.92 11 H 3.27 2 I 2.36 4 J 2.5 3 K 3.47 1 Eval. Pair Correlation Eval. 1 – Eval. 2 0.873365 Eval. 1 – Eval. 3 0.890415 Eval. 2 – Eval. 3 0.905915
    • Results – Image Relevance #13 Team Precision (%) Rank A 54.19 3 B 31.75 5 C 58.09 2 D 52.04 4 E 27.39 6 F 0 8 G 0 8 H 58.82 1 I 0 8 J 0 8 K 18.45 7 Eval. Pair Correlation Eval. 1 – Eval. 2 0.944946 Eval. 1 – Eval. 3 0.919469 Eval. 2 – Eval. 3 0.79596
    • Results – Aggregate (1/2) • For each criterion Ci, we computed the score of each team relative to the best team for this criterion: Ci * (team) = Ci (team) / max(Ci (teamj)) • We then aggregated over the different norm. scores: Ctot = 0.25*Cref*Cpool + 0.25*Cread + 0.25*Ccoh + 0.25*Cdiv where Cref is computed from the recall of reference topics, Cpool from the F-score of the pooled topics, and Cread, Ccoh and Cdiv from readability, coherence and diversity respectively. #14
    • Results – Aggregate (2/2) #15 Team Precision (%) Rank A 0.694 7 B 0.755 4 C 0.710 5 D 0.785 3 E 0.892 1 F 0.614 10 G 0.652 9 H 0.842 2 I 0.662 8 J 0.546 11 K 0.70987 6 We tried several other alternative aggregation scores. The top three teams were the same!
    • Program 15:20-15:30: Carlos Martin-Dancausa and Ayse Goker: Real-time topic detection with bursty n-grams. 16:00-16:20: Gopi Chand Nuttaki, Olfa Nasraoui, Behnoush Abdollahi, Mahsa Badami, Wenlong Sun: Distributed LDA based topic modelling and topic agglomeration in a latent space. 16:20-16:40: Steven van Canneyt, Matthias Feys, Steven Schockaert, Thomas Demeester, Chris Develder, Bart Dhoedt: Detecting newsworthy topics in Twitter. 16:40-17:00: Georgiana Ifrim, Bichen Shi, Igor Brigadir: Event detection in Twitter using aggressive filtering and hierarchical tweet clustering. 17:00-17:20: Gerard Burnside, Dimitrios Milioris, Philippe Jacquet: One day in Twitter: Topic detection via joint complexity. 17:20-17:30: Georgios Petkos, Symeon Papadopoulos, Yiannis Kompatsiaris: Two-level message clustering for topic detection in Twitter. 17:30-17:40: Winners’ announcement! #16
    • Limitations – Lessons Learned • Did not take into account time – However, methods that produce a newsworthy topic earlier should be rewarded • Did not take into account image relevance – since we considered it an optional field • Coherence and diversity had extreme values in numerous cases – e.g. when a single relevant tweet was provided as representative • Evaluation turned out to be a very complex task! • Assessing only five slots (out of the 96) is definitely a compromise: (a) consider use of more evaluators/AMT, (b) consider simpler evaluation tasks #17
    • Plan • Release evaluation resources – list of reference topics – list of pooled newsworthy topics – evaluation scores • Papers – SNOW Data Challenge paper – Resubmission of participants’ papers with CEUR style – Submission to CEUR-ws.org • Open-source implementations? • Further plans? #18
    • Thank you! #19