37,910 research outputs found
Document Filtering for Long-tail Entities
Filtering relevant documents with respect to entities is an essential task in
the context of knowledge base construction and maintenance. It entails
processing a time-ordered stream of documents that might be relevant to an
entity in order to select only those that contain vital information.
State-of-the-art approaches to document filtering for popular entities are
entity-dependent: they rely on and are also trained on the specifics of
differentiating features for each specific entity. Moreover, these approaches
tend to use so-called extrinsic information such as Wikipedia page views and
related entities which is typically only available only for popular head
entities. Entity-dependent approaches based on such signals are therefore
ill-suited as filtering methods for long-tail entities. In this paper we
propose a document filtering method for long-tail entities that is
entity-independent and thus also generalizes to unseen or rarely seen entities.
It is based on intrinsic features, i.e., features that are derived from the
documents in which the entities are mentioned. We propose a set of features
that capture informativeness, entity-saliency, and timeliness. In particular,
we introduce features based on entity aspect similarities, relation patterns,
and temporal expressions and combine these with standard features for document
filtering. Experiments following the TREC KBA 2014 setup on a publicly
available dataset show that our model is able to improve the filtering
performance for long-tail entities over several baselines. Results of applying
the model to unseen entities are promising, indicating that the model is able
to learn the general characteristics of a vital document. The overall
performance across all entities---i.e., not just long-tail entities---improves
upon the state-of-the-art without depending on any entity-specific training
data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on
Information and Knowledge Management. 201
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readersā communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
Information extraction from multimedia web documents: an open-source platform and testbed
The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval
A Hybrid Approach to Domain-Specific Entity Linking
The current state-of-the-art Entity Linking (EL) systems are geared towards
corpora that are as heterogeneous as the Web, and therefore perform
sub-optimally on domain-specific corpora. A key open problem is how to
construct effective EL systems for specific domains, as knowledge of the local
context should in principle increase, rather than decrease, effectiveness. In
this paper we propose the hybrid use of simple specialist linkers in
combination with an existing generalist system to address this problem. Our
main findings are the following. First, we construct a new reusable benchmark
for EL on a corpus of domain-specific conversations. Second, we test the
performance of a range of approaches under the same conditions, and show that
specialist linkers obtain high precision in isolation, and high recall when
combined with generalist linkers. Hence, we can effectively exploit local
context and get the best of both worlds.Comment: SEM'1
- ā¦