1,925 research outputs found
Relation Classification with Limited Supervision
Large reams of unstructured data, for instance in form textual document collections containing entities and relations, exist in many domains. The process of deriving valuable domain insights and intelligence from such documents collections usually involves the extraction of information such as the relations between the entities in such collections. Relation classification is the task of detecting relations between entities. Supervised machine learning models, which have become the tool of choice for relation classification, require substantial quantities of annotated data for each relation in order to perform optimally. For many domains, such quantities of annotated data for relations may not be readily available, and manually curating such annotations may not be practical due to time and cost constraints.
In this work, we develop both model-specific and model-agnostic approaches for relation classification with limited supervision. We start by proposing an approach for learning embeddings for contextual surface patterns, which are the set of surface patterns associated with entity pairs across a text corpus, to provide additional supervision signals for relation classification with limited supervision. We find that this approach improves classification performance on relations with limited supervision instances. However, this initial approach assumes the availability of at least one annotated instance per relation during training. In order to address this limitation, we propose an approach which formulates the task of relation classification as that of textual entailment. This reformulation allows us to use the textual descriptions of relations to classify their instances. It also allows us to utilize existing textual entailment datasets and models to classify relations with zero supervision instances.
The two methods proposed previously rely on the use of specific model architectures for relation classification. Since a wide variety of models have been proposed for relation classification in the literature, a more general approach is thus desirable. We subsequently propose our first model-agnostic meta-learning algorithm for relation classification with limited supervision. This algorithm is applicable to any gradient-optimized relation classification model. We show that the proposed approach improves the predictive performance of two existing relation classification models when supervision for relations is limited. Next, because all the approaches we have proposed so far assume the availability of all supervision needed for classifying relations prior to model training, they are unable to handle the case when new supervision for relations becomes available after training. Such new supervision may need to be incorporated into the model to enable it classify new relations or to improve its performance on existing relations. Our last approach addresses this short-coming. We propose a model-agnostic algorithm which enables relation classification models to learn continually from new supervision as it becomes available, while doing so in a data-efficient manner and without forgetting knowledge of previous relations
Graph Neural Networks with Generated Parameters for Relation Extraction
Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning
Context-Aware Embeddings for Automatic Art Analysis
Automatic art analysis aims to classify and retrieve artistic representations
from a collection of images by using computer vision and machine learning
techniques. In this work, we propose to enhance visual representations from
neural networks with contextual artistic information. Whereas visual
representations are able to capture information about the content and the style
of an artwork, our proposed context-aware embeddings additionally encode
relationships between different artistic attributes, such as author, school, or
historical period. We design two different approaches for using context in
automatic art analysis. In the first one, contextual data is obtained through a
multi-task learning model, in which several attributes are trained together to
find visual relationships between elements. In the second approach, context is
obtained through an art-specific knowledge graph, which encodes relationships
between artistic attributes. An exhaustive evaluation of both of our models in
several art analysis problems, such as author identification, type
classification, or cross-modal retrieval, show that performance is improved by
up to 7.3% in art classification and 37.24% in retrieval when context-aware
embeddings are used
Zero-Shot Object Detection by Hybrid Region Embedding
Object detection is considered as one of the most challenging problems in
computer vision, since it requires correct prediction of both classes and
locations of objects in images. In this study, we define a more difficult
scenario, namely zero-shot object detection (ZSD) where no visual training data
is available for some of the target object classes. We present a novel approach
to tackle this ZSD problem, where a convex combination of embeddings are used
in conjunction with a detection framework. For evaluation of ZSD methods, we
propose a simple dataset constructed from Fashion-MNIST images and also a
custom zero-shot split for the Pascal VOC detection challenge. The experimental
results suggest that our method yields promising results for ZSD
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class variance and high within-class variance for the classes of
interest with small sample sizes. We study an even more extreme scenario named
zero-shot learning (ZSL) in which no training example exists for some of the
classes. ZSL aims to build a recognition model for new unseen categories by
relating them to seen classes that were previously learned. We establish this
relation by learning a compatibility function between image features extracted
via a convolutional neural network and auxiliary information that describes the
semantics of the classes of interest by using training samples from the seen
classes. Then, we show how knowledge transfer can be performed for the unseen
classes by maximizing this function during inference. We introduce a new data
set that contains 40 different types of street trees in 1-ft spatial resolution
aerial data, and evaluate the performance of this model with manually annotated
attributes, a natural language model, and a scientific taxonomy as auxiliary
information. The experiments show that the proposed model achieves 14.3%
recognition accuracy for the classes with no training examples, which is
significantly better than a random guess accuracy of 6.3% for 16 test classes,
and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition
and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on
Geoscience and Remote Sensing (TGRS), in press, 201
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