16,596 research outputs found
Multiple Models for Recommending Temporal Aspects of Entities
Entity aspect recommendation is an emerging task in semantic search that
helps users discover serendipitous and prominent information with respect to an
entity, of which salience (e.g., popularity) is the most important factor in
previous work. However, entity aspects are temporally dynamic and often driven
by events happening over time. For such cases, aspect suggestion based solely
on salience features can give unsatisfactory results, for two reasons. First,
salience is often accumulated over a long time period and does not account for
recency. Second, many aspects related to an event entity are strongly
time-dependent. In this paper, we study the task of temporal aspect
recommendation for a given entity, which aims at recommending the most relevant
aspects and takes into account time in order to improve search experience. We
propose a novel event-centric ensemble ranking method that learns from multiple
time and type-dependent models and dynamically trades off salience and recency
characteristics. Through extensive experiments on real-world query logs, we
demonstrate that our method is robust and achieves better effectiveness than
competitive baselines.Comment: In proceedings of the 15th Extended Semantic Web Conference (ESWC
2018
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
readers’ 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
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201
An architecture for life-long user modelling
In this paper, we propose a united architecture for the creation of life-long user profiles. Our architecture combines different steps required for a user prole, including feature extraction and representation, reasoning, recommendation and presentation. We discuss various issues that arise in the context of life-long profiling
An Independent Review of USGS Circular 1370: An Evaluation of the Science Needs to Inform Decisions on Outer Continental Shelf Energy Development in the Chukchi and Beaufort Seas, Alaska
Reviews the U.S. Geological Survey's findings and recommendations on Alaska's Arctic Ocean, including geology, ecology and subsistence, effect of climate change on, and impact of oil spills. Makes recommendations for data management and other issues
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
CHORUS Deliverable 4.4: Report of the 2nd CHORUS Conference
The Second CHORUS Conference and third Yahoo! Research Workshop on the Future of Web Search was held during April 4-5, 2008, in Granvalira, Andorra to discuss future directions in multi-medial information access and other specialised topics in the near future of retrieval. Attendance was at capacity, with 97 participants from 11 countries and 3 continents
- …