249 research outputs found
Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review
The history of metaphor research also marks the evolution of knowledge
infusion research. With the continued advancement of deep learning techniques
in recent years, the natural language processing community has shown great
interest in applying knowledge to successful results in metaphor recognition
tasks. Although there has been a gradual increase in the number of approaches
involving knowledge injection in the field of metaphor recognition, there is a
lack of a complete review article on knowledge injection based approaches.
Therefore, the goal of this paper is to provide a comprehensive review of
research advances in the application of deep learning for knowledge injection
in metaphor recognition tasks. In this paper, we systematically summarize and
generalize the mainstream knowledge and knowledge injection principles, as well
as review the datasets, evaluation metrics, and benchmark models used in
metaphor recognition tasks. Finally, we explore the current issues facing
knowledge injection methods and provide an outlook on future research
directions.Comment: 15 page
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the
motions of microscopic particles, has been applied to various non-physics
domains involving human cognition and decision-making that are inherently
uncertain and exhibit certain non-classical, quantum-like characteristics.
Sentiment analysis is a typical example of such domains. In the last few years,
by leveraging the modeling power of quantum probability (a non-classical
probability stemming from quantum mechanics methodology) and deep neural
networks, a range of novel quantum-cognitively inspired models for sentiment
analysis have emerged and performed well. This survey presents a timely
overview of the latest developments in this fascinating cross-disciplinary
area. We first provide a background of quantum probability and quantum
cognition at a theoretical level, analyzing their advantages over classical
theories in modeling the cognitive aspects of sentiment analysis. Then, recent
quantum-cognitively inspired models are introduced and discussed in detail,
focusing on how they approach the key challenges of the sentiment analysis
task. Finally, we discuss the limitations of the current research and highlight
future research directions
Verbal multiword expressions for identification of metaphor
© 2020 The Authors. Published by Association for Computational Linguistics. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: http://dx.doi.org/10.18653/v1/2020.acl-main.259Metaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of
a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first “MWE-aware” metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets
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