25,069 research outputs found
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
How to identify semantic relations among entities in a document when only a
few labeled documents are available? Few-shot document-level relation
extraction (FSDLRE) is crucial for addressing the pervasive data scarcity
problem in real-world scenarios. Metric-based meta-learning is an effective
framework widely adopted for FSDLRE, which constructs class prototypes for
classification. However, existing works often struggle to obtain class
prototypes with accurate relational semantics: 1) To build prototype for a
target relation type, they aggregate the representations of all entity pairs
holding that relation, while these entity pairs may also hold other relations,
thus disturbing the prototype. 2) They use a set of generic NOTA
(none-of-the-above) prototypes across all tasks, neglecting that the NOTA
semantics differs in tasks with different target relation types. In this paper,
we propose a relation-aware prototype learning method for FSDLRE to strengthen
the relational semantics of prototype representations. By judiciously
leveraging the relation descriptions and realistic NOTA instances as guidance,
our method effectively refines the relation prototypes and generates
task-specific NOTA prototypes. Extensive experiments demonstrate that our
method outperforms state-of-the-art approaches by average 2.61% across
various settings of two FSDLRE benchmarks.Comment: Accepted to EMNLP 202
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been used to define several variants of the Self Organizing Map (SOM). This
paper reviews those variants in using a common set of notations in order to
outline differences and similarities between them. It discusses the advantages
and drawbacks of the variants, as well as the actual relevance of the
dissimilarity/kernel SOM for practical applications
Neural Networks for Complex Data
Artificial neural networks are simple and efficient machine learning tools.
Defined originally in the traditional setting of simple vector data, neural
network models have evolved to address more and more difficulties of complex
real world problems, ranging from time evolving data to sophisticated data
structures such as graphs and functions. This paper summarizes advances on
those themes from the last decade, with a focus on results obtained by members
of the SAMM team of Universit\'e Paris
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Few-shot learning (FSL) aims to develop a learning model with the ability to
generalize to new classes using a few support samples. For transductive FSL
tasks, prototype learning and label propagation methods are commonly employed.
Prototype methods generally first learn the representative prototypes from the
support set and then determine the labels of queries based on the metric
between query samples and prototypes. Label propagation methods try to
propagate the labels of support samples on the constructed graph encoding the
relationships between both support and query samples. This paper aims to
integrate these two principles together and develop an efficient and robust
transductive FSL approach, termed Prototype-based Soft-label Propagation
(PSLP). Specifically, we first estimate the soft-label presentation for each
query sample by leveraging prototypes. Then, we conduct soft-label propagation
on our learned query-support graph. Both steps are conducted progressively to
boost their respective performance. Moreover, to learn effective prototypes for
soft-label estimation as well as the desirable query-support graph for
soft-label propagation, we design a new joint message passing scheme to learn
sample presentation and relational graph jointly. Our PSLP method is
parameter-free and can be implemented very efficiently. On four popular
datasets, our method achieves competitive results on both balanced and
imbalanced settings compared to the state-of-the-art methods. The code will be
released upon acceptance
Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition
The SP theory of computing and cognition, described in previous publications,
is an attractive model for intelligent databases because it provides a simple
but versatile format for different kinds of knowledge, it has capabilities in
artificial intelligence, and it can also function like established database
models when that is required.
This paper describes how the SP model can emulate other models used in
database applications and compares the SP model with those other models. The
artificial intelligence capabilities of the SP model are reviewed and its
relationship with other artificial intelligence systems is described. Also
considered are ways in which current prototypes may be translated into an
'industrial strength' working system
SensEmbed: Learning sense embeddings for word and relational similarity
Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including semantic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In addition, their learning process usually relies on massive corpora only, preventing them from taking advantage of structured knowledge. We address both issues by proposing a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement. We evaluate our approach on word similarity and relational similarity frameworks, reporting state-of-the-art performance on multiple datasets
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