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AdvisorElsayed, Tamer
AuthorSUWAILEH, REEM ALI
Available date2018-11-13T06:30:48Z
Publication Date2018-06
URIhttp://hdl.handle.net/10576/11177
AbstractReal-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models. Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time. We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used. Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.
SponsorThis work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation).
Languageen
SubjectREAL-TIME TWEET -FILTERING
SubjectReal-time tweet summarization systems (RTS)
TitleON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION
TypeMaster Thesis
DepartmentComputer Science


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