2015 資訊檢索研討會
2015 IR Workshop
[English]
[研討會簡介]
隨著全球資訊網快速蓬勃的發展,各式各樣的資訊內容和服務不斷地擴增,人 類的生活和互動方式逐漸轉移到網路平台,並且伴隨著無線網路和多媒體技術的 快速進展,傳統的資訊檢索技術也不斷地和這些新的資訊媒體和平台結合,產生許多創新的研究,這些創新研究和重要應用議題仍然受到學術界和產業界重視和熱烈討論。因此,本研討會將邀請國內相關學者專家進行觀念和技術交流。本研討會係繼2002年「資訊自動分類技術研討會」、2003年「資訊檢索與電腦輔助語 言教學研討會」、2004年「文件探勘技術研討會」、2005年「網路資訊檢索技術與趨勢研討會」、2006年「網路探勘技術與趨勢研討會」、2007年「Web 2.0技術 與應用研討會」、2008年「網路社群服務計算暨探勘技術研討會」、2009年「行動資訊檢索暨行動定位服務技術研討會」、2010年「2010資訊檢索創新技術研討會」、2011年「音樂資訊檢索暨社群服務技術研討會」以及2013、2014年「資訊檢索頂尖論文研討會」後續的年度會議活動,在兩年的頂尖論文主題獲得廣大迴響後,於異質資訊產生與流通大爆發,跨領域技術益顯重要的現今,以跨領域技術新趨勢為主題舉辦本屆研討會,並新增「產業面對面」的演講時段,以促進產學交流,鼓勵學生投入自然語言處理與資訊檢索的研究領域,歡迎各界人士踴躍前來參加。
會議主題: |
跨領域自然語言處理與資訊檢索技術新趨勢 |
時間: |
民國104年12月18日(星期五) |
地點: |
中央研究院資訊科學研究所新館106 演講廳 |
主辦單位: |
中央研究院資訊科學研究所 臺灣大學資訊工程系 |
主辦人: |
鄭卜壬 教授 (臺灣大學資訊工程系) |
8:50-9:10 |
Registration |
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9:10-9:20 |
Opening |
鄭卜壬 教授 古倫維 博士 |
臺灣大學資訊工程系 中央研究院資訊所 |
9:20-10:20 |
Keynote Speech: Jointly Modeling Topics,
Events and User Interests on Twitter |
Prof. Jing Jiang |
新加坡管理大學 |
Abstract: With the rapid growth of social
media, Twitter has become one of the most widely adopted platforms for people
to post short and instant messages. Tweets reflect both individual users’
personal interests and major events that have attracted the public’s
attention. In this talk, I will present our recent work on modeling topics,
events and user interests on Twitter. Our work is based on probabilistic
topic models, which have principled theoretical foundations. Based on our
observations with Twitter, we introduced several important modifications to
standard models in order to capture Twitter’s special properties. Our
experiments showed that our models can effectively identify meaningful
events, group events by topics, and potentially recommend events to users
based on their topical interests. |
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10:20-10:40 |
Coffee Break |
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10:40-11:30 |
Invited Talk 1: A Community-based Method for
Valence-Arousal Prediction of Affective Words |
禹良治 教授 |
元智大學資訊管理系 |
Abstract: Compared to the categorical
approach that represents affective states as several discrete classes (e.g.,
positive and negative), the dimensional approach represents affective states
as continuous numerical values in multiple dimensions, such as the
valence-arousal (VA) space, thus allowing for more fine-grained sentiment
analysis. In building dimensional sentiment applications, affective lexicons
with valence-arousal ratings are useful resources but are still very rare.
Several semi-supervised methods such as the kernel method, linear regression,
and the pagerank algorithm have been investigated
to automatically determine the VA ratings of affective words from a set of
semantically similar seed words. These methods suffer from two major
limitations. First, they apply an equal weight to all seeds similar to an
unseen word in predicting its VA ratings. Second, even similar seeds may have
quite different ratings (or an inverse polarity) of valence/arousal to the
unseen word, thus reducing prediction performance. To overcome these
limitations, this study proposes a community-based weighted graph model that
can select seeds which are both similar to and have similar ratings (or the
same polarity) with each unseen word to form a community (sub-graph) so that
its VA ratings can be estimated from such high-quality seeds using a weighted
propagation scheme. That is, seeds more similar to unseen words contribute
more to the estimation process. Experimental results show that the proposed
method yields better prediction performance for both English and Chinese
datasets. |
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11:30-12:20 |
Invited Talk 2 |
林軒田 教授 |
臺灣大學資訊工程系 |
Abstract: Active learning is an important technique that helps reduce
labeling efforts in machine learning applications. Currently, most active
learning strategies are constructed based on some human-designed philosophy;
that is, they reflect what human beings assume to be "good labeling
questions." However, given that a single human-designed philosophy is
unlikely to work on all scenarios, choosing and blending those strategies
under different scenarios is an important but challenging practical task.
This paper tackles this task by letting the machines adaptively
"learn" from the performance of a set of given strategies on a
particular data set. More specifically, we design a learning algorithm that
connects active learning with the well-known multi-armed bandit problem.
Further, we postulate that, given an appropriate choice for the multi-armed
bandit learner, it is possible to estimate the performance of different
strategies on the fly. Extensive empirical studies of the resulting algorithm
confirm that it performs better than strategies that are based on
human-designed philosophy. |
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12:20-13:50 |
Lunch |
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13:50-14:50 |
Invited Talk 3 Meet Industry People: Capture the great moment
of social network efficiently and effectively |
周世恩 先生 |
QSearch,
Co-Founder |
Abstract: Social
media has greatly changed the way we communicate and huge amount of social
behavior data is thus recorded and accumulated simultaneously. The data is
now widely applied to many emerging research issues in combination with
social behavior analysis. More recently, time domain analysis is especially
popular on conducting behavior change investigation, in which people take
snapshots on a particular subject of network on regular intervals, and hot
messages (posts) are in urgent need of snapshot so as to precisely learn
about user’s behavior as time moves. Scraping social networking sites such as
Twitter, Facebook, etc. is not an easy task for data acquisition departments
of most institutions since these sites often have complex structures and also
restrict the amount and frequency of the data that they let out to common
crawlers. In this talk, I will introduce the proposed crawling algorithm and
address the challenges of large-scale dynamic content crawling. |
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14:50-15:10 |
Coffee Break |
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15:10-16:00 |
Invited Talk 4 A Novel Social Influence
Model based on Multiple States and Negative Social Influences |
黃仁暐 教授 |
成功大學電機系 |
Abstract: Social influence has been a
significant and popular topic in the social network analysis. People usually rely
on the social influence model to predict and learn the influence diffusion
process in real world. We studied the most popular influence models nowadays
and found out some limitations of the traditional influence models.
Traditional models only categorize nodes into two types of states, say active
and inactive states. In addition, most previous models have only taken
positive influences into account. We try to break the above limitations and
propose a novel social influence model based on multiple states and negative
social influence. According to the new propagation method, the strength of
the social influence may be reduced over time. The experimental results show
that the proposed model outperforms previous models in precisions of
prediction. |
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16:00-16:50 |
Invited Talk 5 Using Non-Verbal Information
to Augment Designs of Language-based Interactions |
王浩全 教授 |
清華大學資訊工程系及 |
Abstract: Language plays an important
role in communications and interactions, but communications don’t necessarily
have to stay verbal to be the best. In human-human communication, for
example, verbal messages can be supplemented in non-verbal ways, such as
facial expressions, gestures or emoticons/emojis in
online communication today. In human-machine communication like user
interface design, designers also use graphic icons to communicate with users
what are the intended functions of specific interface features. Because
non-verbal information can inform the users what language refers, intends to
mean, or tries to emphasize, we see opportunities in using non-verbal
information to functionally augment language-based interactions when the
problems in expression and comprehension are severe. In this talk, I will
present a view on how non-verbal and verbal information might be coordinated
and integrated in interaction designs. I’ll showcase a number of examples
that we pursued in the Collaborative and Social Computing Lab at National
Tsing Hua University to illustrate the benefits and constraints of injecting
non-verbal information to verbal interactions as a general design pattern. |
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16:50-17:00 |
Closing |
鄭卜壬 教授 古倫維 博士 |
臺灣大學資訊工程系 中央研究院資訊所 |
報名資訊:
§ 報名費:一般人士 (會員600元,非會員 800元);學生 (會員 400元,非會員 600元)
§ 線上報名及繳費截止日:12月 13日。
§ 報名方式:線上報名已截止,歡迎至現場報名,報名費加收200元。
§ 繳費方式:
郵政劃撥:戶名:中華民國計算語言學學會,帳號:19166251
傳真信用卡繳費單:下載繳費單
聯絡資訊:
中研院資訊所 黃琪小姐( [email protected]),
TEL:02-2788-3799 ext. 1502