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UID:calendar.346291.field_event_date.0@www.wright.edu
DTSTAMP:20260219T225746Z
CREATED:20181113T211536Z
DESCRIPTION:Ph.D. Committee:Ā  Drs. Amit Sheth\, Advisor\, Derek Doran\, Kri
 shnaprasad Thirunarayan\, and Wenbo Wang (GoDaddy Inc.) Thesis Statement: 
 Machine-readable emoji sense repositories can be created and used to enabl
 e a substantially better understanding of the emoji meaning in text contex
 ts. This is useful for improving the performance of downstream application
 s such as emoji sense disambiguation and calculating emoji similarity.Abst
 ractThe ability to automatically process and interpret text fused with emo
 ji will be essential as society embraces emoji as a standard form of onlin
 e communication. Since their introduction in the late 1990's\, emoji have 
 been widely used to enhance the sentiment\, emotion\, and sarcasm expresse
 d in social media messages. They are equally popular across many social me
 dia sites including Facebook\, Instagram\, and Twitter. Processing emoji u
 sing traditional Natural Language Processing (NLP) techniques is a challen
 ging task due to the pictorial nature of emoji and the fact that (the same
 ) emoji may be used in different contexts and cultures to express differen
 t meanings. Their polysemous nature complicates tasks such as emoji simila
 rity calculation and emoji sense disambiguation. Having access to machine-
 readable sense repositories that are specifically designed to capture emoj
 i meaning can play a vital role in representing\, contextually disambiguat
 ing\, and converting pictorial forms of emoji into text\, enabling NLP tec
 hniques to process this new medium of communication.Ā Ā This dissertation pr
 esents EmojiNet\, the largest machine-readable emoji sense inventory that 
 links Unicode emoji representations to English meanings extracted from rel
 iable online web sources. EmojiNet consists of: (i) 12\,904 sense labels o
 ver 2\,389 emoji linked to machine-readable sense definitions seen in Babe
 lNet\; (ii) context words associated with emoji senses based on word embed
 ding models\; and (iii) for some emoji\, discrepancies in their presentati
 on on different platforms. It further presents methods for emoji similarit
 y evaluation and sense disambiguation uniquely enabled by EmojiNet.Ā  Emoji
  similarity methods are formed using word embedding models and are evaluat
 ed over a number of corpora. Those same embedding models are further used 
 to carry out accuracy of emoji sense disambiguation. The EmojiNet framewor
 k\, its RESTful web service\, and benchmark datasets created as part of th
 is dissertation are publicly released at http://emojinet.knoesis.org/.Rele
 vant publications: http://knoesis.org/Library?f%5Bsearch%5D=Sanjaya
DTSTART;TZID=America/New_York:20181119T100000
DTEND;TZID=America/New_York:20181119T120000
LAST-MODIFIED:20181114T141416Z
LOCATION:366 Joshi
SUMMARY:Ph.D. Dissertation Defense ā€œA Framework to Understand Emoji Meaning
 : Similarity and Sense Disambiguation of Emoji using EmojiNetā€ By Sanjaya 
 Wijeratne
URL;TYPE=URI:/events/phd-dissertation-defense-%E2%80%
 9C-framework-understand-emoji-meaning-similarity-sense-disambiguation
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