17,033 research outputs found
Semantic Tagging on Historical Maps
Tags assigned by users to shared content can be ambiguous. As a possible
solution, we propose semantic tagging as a collaborative process in which a
user selects and associates Web resources drawn from a knowledge context. We
applied this general technique in the specific context of online historical
maps and allowed users to annotate and tag them. To study the effects of
semantic tagging on tag production, the types and categories of obtained tags,
and user task load, we conducted an in-lab within-subject experiment with 24
participants who annotated and tagged two distinct maps. We found that the
semantic tagging implementation does not affect these parameters, while
providing tagging relationships to well-defined concept definitions. Compared
to label-based tagging, our technique also gathers positive and negative
tagging relationships. We believe that our findings carry implications for
designers who want to adopt semantic tagging in other contexts and systems on
the Web.Comment: 10 page
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty
Entity Linking (EL) is the task of automatically identifying entity mentions
in a piece of text and resolving them to a corresponding entity in a reference
knowledge base like Wikipedia. There is a large number of EL tools available
for different types of documents and domains, yet EL remains a challenging task
where the lack of precision on particularly ambiguous mentions often spoils the
usefulness of automated disambiguation results in real applications. A priori
approximations of the difficulty to link a particular entity mention can
facilitate flagging of critical cases as part of semi-automated EL systems,
while detecting latent factors that affect the EL performance, like
corpus-specific features, can provide insights on how to improve a system based
on the special characteristics of the underlying corpus. In this paper, we
first introduce a consensus-based method to generate difficulty labels for
entity mentions on arbitrary corpora. The difficulty labels are then exploited
as training data for a supervised classification task able to predict the EL
difficulty of entity mentions using a variety of features. Experiments over a
corpus of news articles show that EL difficulty can be estimated with high
accuracy, revealing also latent features that affect EL performance. Finally,
evaluation results demonstrate the effectiveness of the proposed method to
inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP
Symposium On Applied Computing (SAC 2019
Text categorization and similarity analysis: similarity measure, literature review
Document classification and provenance has become an important area of computer science as the amount of digital information is growing significantly. Organisations are storing documents on computers rather than in paper form. Software is now required that will show the similarities between documents (i.e. document classification) and to point out duplicates and possibly the history of each document (i.e. provenance). Poor organisation is common and leads to situations like above. There exists a number of software solutions in this area designed to make document organisation as simple as possible. I'm doing my project with Pingar who are a company based in Auckland who aim to help organise the growing amount of unstructured digital data. This reports analyses the existing literature in this area with the aim to determine what already exists and how my project will be different from existing solutions
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
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