83,236 research outputs found
Weakly-supervised appraisal analysis
This article is concerned with the computational treatment of Appraisal, a Systemic Functional Linguistic theory of the types of language employed to communicate opinion in English. The theory considers aspects such as Attitude (how writers communicate their point of view), Engagement (how writers align themselves with respect to the opinions of others) and Graduation (how writers amplify or diminish their attitudes and engagements). To analyse text according to the theory we employ a weakly-supervised approach to text classification, which involves comparing the similarity of words with prototypical examples of classes. We evaluate the method's performance using a collection of book reviews annotated according to the Appraisal theory
Using distributional similarity to organise biomedical terminology
We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy
Entity Type Prediction in Knowledge Graphs using Embeddings
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized
as the backbone of diverse applications in the field of data mining and
information retrieval. Hence, the completeness and correctness of the Knowledge
Graphs (KGs) are vital. Most of these KGs are mostly created either via an
automated information extraction from Wikipedia snapshots or information
accumulation provided by the users or using heuristics. However, it has been
observed that the type information of these KGs is often noisy, incomplete, and
incorrect. To deal with this problem a multi-label classification approach is
proposed in this work for entity typing using KG embeddings. We compare our
approach with the current state-of-the-art type prediction method and report on
experiments with the KGs
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be achieved on such categories even without labeled training
data. Given image and class embeddings, we learn a compatibility function such
that matching embeddings are assigned a higher score than mismatching ones;
zero-shot classification of an image proceeds by finding the label yielding the
highest joint compatibility score. We use state-of-the-art image features and
focus on different supervised attributes and unsupervised output embeddings
either derived from hierarchies or learned from unlabeled text corpora. We
establish a substantially improved state-of-the-art on the Animals with
Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate
that purely unsupervised output embeddings (learned from Wikipedia and improved
with fine-grained text) achieve compelling results, even outperforming the
previous supervised state-of-the-art. By combining different output embeddings,
we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for
Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and
Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed
and Daniel Walter and Honglak Lee and Bernt Schiele}
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual
exclusion of labels, hierarchical multi-label classification (HMLC) aims to
assign multiple labels to every instance with the labels organized under
hierarchical relations. Besides the labels, since linguistic ontologies are
intrinsic hierarchies, the conceptual relations between words can also form
hierarchical structures. Thus it can be a challenge to learn mappings from word
hierarchies to label hierarchies. We propose to model the word and label
hierarchies by embedding them jointly in the hyperbolic space. The main reason
is that the tree-likeness of the hyperbolic space matches the complexity of
symbolic data with hierarchical structures. A new Hyperbolic Interaction Model
(HyperIM) is designed to learn the label-aware document representations and
make predictions for HMLC. Extensive experiments are conducted on three
benchmark datasets. The results have demonstrated that the new model can
realistically capture the complex data structures and further improve the
performance for HMLC comparing with the state-of-the-art methods. To facilitate
future research, our code is publicly available
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