2,698 research outputs found
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
Embedding models typically associate each word with a single real-valued
vector, representing its different properties. Evaluation methods, therefore,
need to analyze the accuracy and completeness of these properties in
embeddings. This requires fine-grained analysis of embedding subspaces.
Multi-label classification is an appropriate way to do so. We propose a new
evaluation method for word embeddings based on multi-label classification given
a word embedding. The task we use is fine-grained name typing: given a large
corpus, find all types that a name can refer to based on the name embedding.
Given the scale of entities in knowledge bases, we can build datasets for this
task that are complementary to the current embedding evaluation datasets in:
they are very large, contain fine-grained classes, and allow the direct
evaluation of embeddings without confounding factors like sentence contextComment: 6 pages, The 3rd Workshop on Representation Learning for NLP
(RepL4NLP @ ACL2018
A matter of words: NLP for quality evaluation of Wikipedia medical articles
Automatic quality evaluation of Web information is a task with many fields of
applications and of great relevance, especially in critical domains like the
medical one. We move from the intuition that the quality of content of medical
Web documents is affected by features related with the specific domain. First,
the usage of a specific vocabulary (Domain Informativeness); then, the adoption
of specific codes (like those used in the infoboxes of Wikipedia articles) and
the type of document (e.g., historical and technical ones). In this paper, we
propose to leverage specific domain features to improve the results of the
evaluation of Wikipedia medical articles. In particular, we evaluate the
articles adopting an "actionable" model, whose features are related to the
content of the articles, so that the model can also directly suggest strategies
for improving a given article quality. We rely on Natural Language Processing
(NLP) and dictionaries-based techniques in order to extract the bio-medical
concepts in a text. We prove the effectiveness of our approach by classifying
the medical articles of the Wikipedia Medicine Portal, which have been
previously manually labeled by the Wiki Project team. The results of our
experiments confirm that, by considering domain-oriented features, it is
possible to obtain sensible improvements with respect to existing solutions,
mainly for those articles that other approaches have less correctly classified.
Other than being interesting by their own, the results call for further
research in the area of domain specific features suitable for Web data quality
assessment
Event-based Access to Historical Italian War Memoirs
The progressive digitization of historical archives provides new, often
domain specific, textual resources that report on facts and events which have
happened in the past; among these, memoirs are a very common type of primary
source. In this paper, we present an approach for extracting information from
Italian historical war memoirs and turning it into structured knowledge. This
is based on the semantic notions of events, participants and roles. We evaluate
quantitatively each of the key-steps of our approach and provide a graph-based
representation of the extracted knowledge, which allows to move between a Close
and a Distant Reading of the collection.Comment: 23 pages, 6 figure
Improving Entity Linking through Semantic Reinforced Entity Embeddings
Entity embeddings, which represent different aspects of each entity with a
single vector like word embeddings, are a key component of neural entity
linking models. Existing entity embeddings are learned from canonical Wikipedia
articles and local contexts surrounding target entities. Such entity embeddings
are effective, but too distinctive for linking models to learn contextual
commonality. We propose a simple yet effective method, FGS2EE, to inject
fine-grained semantic information into entity embeddings to reduce the
distinctiveness and facilitate the learning of contextual commonality. FGS2EE
first uses the embeddings of semantic type words to generate semantic
embeddings, and then combines them with existing entity embeddings through
linear aggregation. Extensive experiments show the effectiveness of such
embeddings. Based on our entity embeddings, we achieved new sate-of-the-art
performance on entity linking.Comment: 6 pages, 3 figures, ACL 202
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