Proceedings ArticleDOI
Incorporating Social Context and Domain Knowledge for Entity Recognition
Jie Tang,Zhanpeng Fang,Jimeng Sun +2 more
- pp 517-526
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TLDR
The SOCINST model, which can automatically construct a context of subtopics for each instance, with each subtopic representing one possible meaning of the instance, is proposed and incorporated into the model using a Dirichlet tree distribution.Abstract:
Recognizing entity instances in documents according to a knowledge base is a fundamental problem in many data mining applications. The problem is extremely challenging for short documents in complex domains such as social media and biomedical domains. Large concept spaces and instance ambiguity are key issues that need to be addressed. Most of the documents are created in a social context by common authors via social interactions, such as reply and citations. Such social contexts are largely ignored in the instance-recognition literature. How can users' interactions help entity instance recognition? How can the social context be modeled so as to resolve the ambiguity of different instances? In this paper, we propose the SOCINST model to formalize the problem into a probabilistic model. Given a set of short documents (e.g., tweets or paper abstracts) posted by users who may connect with each other, SOCINST can automatically construct a context of subtopics for each instance, with each subtopic representing one possible meaning of the instance. The model is also able to incorporate social relationships between users to help build social context. We further incorporate domain knowledge into the model using a Dirichlet tree distribution. We evaluate the proposed model on three different genres of datasets: ICDM'12 Contest, Weibo, and I2B2. In ICDM'12 Contest, the proposed model clearly outperforms (+21.4%; $p l 1e-5 with t-test) all the top contestants. In Weibo and I2B2, our results also show that the recognition accuracy of SOCINST is up to 5.3-26.6% better than those of several alternative methods.read more
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Journal ArticleDOI
TempoRec: Temporal-Topic Based Recommender for Social Network Services
Yin Zhang,Zhixiao Tu,Qian Wang +2 more
TL;DR: A hybrid recommendation algorithm based on social relations and time-sequenced topics, which has been verified using Real Sina Weibo datasets, works well and achieves better mean average precision (MAP) than existing other counterparts.
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Knowledge Graphs: An Information Retrieval Perspective
TL;DR: An overview of the literature on knowledge graphs (KGs) in the context of information retrieval (IR) is provided and how KGs can be employed to support IR tasks, including document and entity retrieval is discussed.
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Multi-modal Bayesian embeddings for learning social knowledge graphs
TL;DR: A multi-modal Bayesian embedding model, GenVector, is proposed to learn latent topics that generate word and network embeddings in a shared latent topic space, and significantly decreases the error rate in an online A/B test with live users.
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AMiner: Mining Deep Knowledge from Big Scholar Data
TL;DR: This talk will focus on answering two fundamental questions for author-centric network analysis: who is who?
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A semantic and social‐based collaborative recommendation of friends in social networks
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References
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Named Entity Recognition in Tweets: An Experimental Study
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