36,792 research outputs found
Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia
Hyperlinks are an essential feature of the World Wide Web. They are
especially important for online encyclopedias such as Wikipedia: an article can
often only be understood in the context of related articles, and hyperlinks
make it easy to explore this context. But important links are often missing,
and several methods have been proposed to alleviate this problem by learning a
linking model based on the structure of the existing links. Here we propose a
novel approach to identifying missing links in Wikipedia. We build on the fact
that the ultimate purpose of Wikipedia links is to aid navigation. Rather than
merely suggesting new links that are in tune with the structure of existing
links, our method finds missing links that would immediately enhance
Wikipedia's navigability. We leverage data sets of navigation paths collected
through a Wikipedia-based human-computation game in which users must find a
short path from a start to a target article by only clicking links encountered
along the way. We harness human navigational traces to identify a set of
candidates for missing links and then rank these candidates. Experiments show
that our procedure identifies missing links of high quality
Entity Linking for Queries by Searching Wikipedia Sentences
We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles
and directly use the human-annotated entities in the similar sentences as
candidate entities for the query. Then, we employ a rich set of features, such
as link-probability, context-matching, word embeddings, and relatedness among
candidate entities as well as their related entities, to rank the candidates
under a regression based framework. The advantages of our approach lie in two
aspects, which contribute to the ranking process and final linking result.
First, it can greatly reduce the number of candidate entities by filtering out
irrelevant entities with the words in the query. Second, we can obtain the
query sensitive prior probability in addition to the static link-probability
derived from all Wikipedia articles. We conduct experiments on two benchmark
datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ
dataset. Experimental results show that our method outperforms state-of-the-art
systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ
dataset
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
WikiM: Metapaths based Wikification of Scientific Abstracts
In order to disseminate the exponential extent of knowledge being produced in
the form of scientific publications, it would be best to design mechanisms that
connect it with already existing rich repository of concepts -- the Wikipedia.
Not only does it make scientific reading simple and easy (by connecting the
involved concepts used in the scientific articles to their Wikipedia
explanations) but also improves the overall quality of the article. In this
paper, we present a novel metapath based method, WikiM, to efficiently wikify
scientific abstracts -- a topic that has been rarely investigated in the
literature. One of the prime motivations for this work comes from the
observation that, wikified abstracts of scientific documents help a reader to
decide better, in comparison to the plain abstracts, whether (s)he would be
interested to read the full article. We perform mention extraction mostly
through traditional tf-idf measures coupled with a set of smart filters. The
entity linking heavily leverages on the rich citation and author publication
networks. Our observation is that various metapaths defined over these networks
can significantly enhance the overall performance of the system. For mention
extraction and entity linking, we outperform most of the competing
state-of-the-art techniques by a large margin arriving at precision values of
72.42% and 73.8% respectively over a dataset from the ACL Anthology Network. In
order to establish the robustness of our scheme, we wikify three other datasets
and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for
the mention extraction and the entity linking phase
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
Many fundamental problems in natural language processing rely on determining
what entities appear in a given text. Commonly referenced as entity linking,
this step is a fundamental component of many NLP tasks such as text
understanding, automatic summarization, semantic search or machine translation.
Name ambiguity, word polysemy, context dependencies and a heavy-tailed
distribution of entities contribute to the complexity of this problem.
We here propose a probabilistic approach that makes use of an effective
graphical model to perform collective entity disambiguation. Input mentions
(i.e.,~linkable token spans) are disambiguated jointly across an entire
document by combining a document-level prior of entity co-occurrences with
local information captured from mentions and their surrounding context. The
model is based on simple sufficient statistics extracted from data, thus
relying on few parameters to be learned.
Our method does not require extensive feature engineering, nor an expensive
training procedure. We use loopy belief propagation to perform approximate
inference. The low complexity of our model makes this step sufficiently fast
for real-time usage. We demonstrate the accuracy of our approach on a wide
range of benchmark datasets, showing that it matches, and in many cases
outperforms, existing state-of-the-art methods
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