1,324 research outputs found
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives
Name ambiguity is common in academic digital libraries, such as multiple
authors having the same name. This creates challenges for academic data
management and analysis, thus name disambiguation becomes necessary. The
procedure of name disambiguation is to divide publications with the same name
into different groups, each group belonging to a unique author. A large amount
of attribute information in publications makes traditional methods fall into
the quagmire of feature selection. These methods always select attributes
artificially and equally, which usually causes a negative impact on accuracy.
The proposed method is mainly based on representation learning for
heterogeneous networks and clustering and exploits the self-attention
technology to solve the problem. The presentation of publications is a
synthesis of structural and semantic representations. The structural
representation is obtained by meta-path-based sampling and a skip-gram-based
embedding method, and meta-path level attention is introduced to automatically
learn the weight of each feature. The semantic representation is generated
using NLP tools. Our proposal performs better in terms of name disambiguation
accuracy compared with baselines and the ablation experiments demonstrate the
improvement by feature selection and the meta-path level attention in our
method. The experimental results show the superiority of our new method for
capturing the most attributes from publications and reducing the impact of
redundant information
Fuzzy Ants Clustering for Web People Search
A search engine query for a person’s name often brings up web pages corresponding to several people who share the same name. The Web People Search (WePS) problem involves organizing such search results for an ambiguous name query in meaningful clusters, that group together all web pages corresponding to one single individual. A particularly challenging aspect of this task is that it is in general not known beforehand how many clusters to expect. In this paper we therefore propose the use of a Fuzzy Ants clustering algorithm that does not rely on prior knowledge of the number of clusters that need to be found in the data. An evaluation on benchmark data sets from SemEval’s WePS1 and WePS2 competitions shows that the resulting system is competitive with the agglomerative clustering Agnes algorithm. This is particularly interesting as the latter involves manual setting of a similarity threshold (or estimating the number of clusters in advance) while the former does not
Linking named entities to Wikipedia
Natural language is fraught with problems of ambiguity, including name reference. A name in text can refer to multiple entities just as an entity can be known by different names. This thesis examines how a mention in text can be linked to an external knowledge base (KB), in our case, Wikipedia. The named entity linking (NEL) task requires systems to identify the KB entry, or Wikipedia article, that a mention refers to; or, if the KB does not contain the correct entry, return NIL. Entity linking systems can be complex and we present a framework for analysing their different components, which we use to analyse three seminal systems which are evaluated on a common dataset and we show the importance of precise search for linking. The Text Analysis Conference (TAC) is a major venue for NEL research. We report on our submissions to the entity linking shared task in 2010, 2011 and 2012. The information required to disambiguate entities is often found in the text, close to the mention. We explore apposition, a common way for authors to provide information about entities. We model syntactic and semantic restrictions with a joint model that achieves state-of-the-art apposition extraction performance. We generalise from apposition to examine local descriptions specified close to the mention. We add local description to our state-of-the-art linker by using patterns to extract the descriptions and matching against this restricted context. Not only does this make for a more precise match, we are also able to model failure to match. Local descriptions help disambiguate entities, further improving our state-of-the-art linker. The work in this thesis seeks to link textual entity mentions to knowledge bases. Linking is important for any task where external world knowledge is used and resolving ambiguity is fundamental to advancing research into these problems
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
Measuring the similarity of short written contexts is a fundamental problem
in Natural Language Processing. This article provides a unifying framework by
which short context problems can be categorized both by their intended
application and proposed solution. The goal is to show that various problems
and methodologies that appear quite different on the surface are in fact very
closely related. The axes by which these categorizations are made include the
format of the contexts (headed versus headless), the way in which the contexts
are to be measured (first-order versus second-order similarity), and the
information used to represent the features in the contexts (micro versus macro
views). The unifying thread that binds together many short context applications
and methods is the fact that similarity decisions must be made between contexts
that share few (if any) words in common.Comment: 23 page
An Approach to Web-Scale Named-Entity Disambiguation
We present a multi-pass clustering approach to large scale. wide-scope named-entity disambiguation (NED) oil collections of web pages. Our approach Uses name co-occurrence information to cluster and hence disambiguate entities. and is designed to handle NED on the entire web. We show that on web collections, NED becomes increasing), difficult as the corpus size increases, not only because of the challenge of scaling the NED algorithm, but also because new and surprising facets of entities become visible in the data. This effect limits the potential benefits for data-driven approaches of processing larger data-sets, and suggests that efficient clustering-based disambiguation methods for the web will require extracting more specialized information front documents
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
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