3,006 research outputs found
Information extraction for social media
The rapid growth in IT in the last two decades has led to a growth in the amount of information available online. A new style for sharing information is social media. Social media is a continuously instantly updated source of information. In this position paper, we propose a framework for Information Extraction (IE) from unstructured user generated contents on social media. The framework proposes solutions to overcome the IE challenges in this domain such as the short context, the noisy sparse contents and the uncertain contents. To overcome the challenges facing IE from social media, State-Of-The-Art approaches need to be adapted to suit the nature of social media posts. The key components and aspects of our proposed framework are noisy text filtering, named entity extraction, named entity disambiguation, feedback loops, and uncertainty handling
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
Collective entity disambiguation aims to jointly resolve multiple mentions by
linking them to their associated entities in a knowledge base. Previous works
are primarily based on the underlying assumption that entities within the same
document are highly related. However, the extend to which these mentioned
entities are actually connected in reality is rarely studied and therefore
raises interesting research questions. For the first time, we show that the
semantic relationships between the mentioned entities are in fact less dense
than expected. This could be attributed to several reasons such as noise, data
sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE,
a new tree-based objective for the entity disambiguation problem. The key
intuition behind MINTREE is the concept of coherence relaxation which utilizes
the weight of a minimum spanning tree to measure the coherence between
entities. Based on this new objective, we design a novel entity disambiguation
algorithms which we call Pair-Linking. Instead of considering all the given
mentions, Pair-Linking iteratively selects a pair with the highest confidence
at each step for decision making. Via extensive experiments, we show that our
approach is not only more accurate but also surprisingly faster than many
state-of-the-art collective linking algorithms
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Distant Supervision for Entity Linking
Entity linking is an indispensable operation of populating knowledge
repositories for information extraction. It studies on aligning a textual
entity mention to its corresponding disambiguated entry in a knowledge
repository. In this paper, we propose a new paradigm named distantly supervised
entity linking (DSEL), in the sense that the disambiguated entities that belong
to a huge knowledge repository (Freebase) are automatically aligned to the
corresponding descriptive webpages (Wiki pages). In this way, a large scale of
weakly labeled data can be generated without manual annotation and fed to a
classifier for linking more newly discovered entities. Compared with
traditional paradigms based on solo knowledge base, DSEL benefits more via
jointly leveraging the respective advantages of Freebase and Wikipedia.
Specifically, the proposed paradigm facilitates bridging the disambiguated
labels (Freebase) of entities and their textual descriptions (Wikipedia) for
Web-scale entities. Experiments conducted on a dataset of 140,000 items and
60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze
the feature performance and improve the F1-measure to 0.545
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