22,034 research outputs found
Towards Deep Semantic Analysis Of Hashtags
Hashtags are semantico-syntactic constructs used across various social
networking and microblogging platforms to enable users to start a topic
specific discussion or classify a post into a desired category. Segmenting and
linking the entities present within the hashtags could therefore help in better
understanding and extraction of information shared across the social media.
However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden),
the segmentation of hashtags into constituent entities ("NSA" and "Edward
Snowden" in this case) is not a trivial task. Most of the current
state-of-the-art social media analytics systems like Sentiment Analysis and
Entity Linking tend to either ignore hashtags, or treat them as a single word.
In this paper, we present a context aware approach to segment and link entities
in the hashtags to a knowledge base (KB) entry, based on the context within the
tweet. Our approach segments and links the entities in hashtags such that the
coherence between hashtag semantics and the tweet is maximized. To the best of
our knowledge, no existing study addresses the issue of linking entities in
hashtags for extracting semantic information. We evaluate our method on two
different datasets, and demonstrate the effectiveness of our technique in
improving the overall entity linking in tweets via additional semantic
information provided by segmenting and linking entities in a hashtag.Comment: To Appear in 37th European Conference on Information Retrieva
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Enriching videos with light semantics
This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description
LODE: Linking Digital Humanities Content to the Web of Data
Numerous digital humanities projects maintain their data collections in the
form of text, images, and metadata. While data may be stored in many formats,
from plain text to XML to relational databases, the use of the resource
description framework (RDF) as a standardized representation has gained
considerable traction during the last five years. Almost every digital
humanities meeting has at least one session concerned with the topic of digital
humanities, RDF, and linked data. While most existing work in linked data has
focused on improving algorithms for entity matching, the aim of the
LinkedHumanities project is to build digital humanities tools that work "out of
the box," enabling their use by humanities scholars, computer scientists,
librarians, and information scientists alike. With this paper, we report on the
Linked Open Data Enhancer (LODE) framework developed as part of the
LinkedHumanities project. With LODE we support non-technical users to enrich a
local RDF repository with high-quality data from the Linked Open Data cloud.
LODE links and enhances the local RDF repository without compromising the
quality of the data. In particular, LODE supports the user in the enhancement
and linking process by providing intuitive user-interfaces and by suggesting
high-quality linking candidates using tailored matching algorithms. We hope
that the LODE framework will be useful to digital humanities scholars
complementing other digital humanities tools
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity
retrieval by re-ranking the result list using graph embeddings. The paper shows
that graph embeddings are useful for entity-oriented search tasks. We
demonstrate empirically that encoding information from the knowledge graph into
(graph) embeddings contributes to a higher increase in effectiveness of entity
retrieval results than using plain word embeddings. We analyze the impact of
the accuracy of the entity linker on the overall retrieval effectiveness. Our
analysis further deploys the cluster hypothesis to explain the observed
advantages of graph embeddings over the more widely used word embeddings, for
user tasks involving ranking entities
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