Results 61 to 70 of about 9,897,879 (386)
Reevaluating Adversarial Examples in Natural Language
, 2020 State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model ...
Ji, Yangfeng +4 more
core +1 more source
Natural Language Ontology [PDF]
, 2017 The aim of natural language ontology is to uncover the ontological categories and structures that are implicit in the use of natural language, that is, that a speaker accepts when using a language.
Moltmann, Friederike
core +1 more source
NATURAL LANGUAGE UNDERSTANDING
AI Magazine, 1980 This is an excerpt from the Handbook of Artificial Intelligence, a compendium of hundreds of articles about AI ideas, techniques, and programs being prepared at Stanford University by AI researchers and students from across the country. In addition to articles describing the specifics of various AI programming methods, the Handbook contains dozens of ...
openaire +3 more sources
Adversarial Generation of Natural Language
, 2017 Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation.
Courville, Aaron +4 more
core +1 more source
Learning Models for Following Natural Language Directions in Unknown Environments [PDF]
, 2015 Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces.
Duvallet, Felix +5 more
core +2 more sources
NLTK: The Natural Language Toolkit
Annual Meeting of the Association for Computational Linguistics, 2006 The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license.
Steven Bird
semanticscholar +1 more source
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment [PDF]
AAAI Conference on Artificial Intelligence, 2019 Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models
Di Jin +3 more
semanticscholar +1 more source
Frontiers in Psychology, 2022 Now, the application of intelligent technologies such as machine learning and deep learning in natural language processing has achieved good results. This article studies the integration of emotion analysis in English module teaching of natural language ...
Fuxing Su
doaj +1 more source
Corela, 2008 Argumentation is situated at the crossroads of various disciplines; it concerns formal logic, as well as the production of texts appropriate to a given communicative situation, and even the interpretation of texts.
Sara Boutouhami, Daniel Kayser
doaj +1 more source
Robust Processing of Natural Language
, 1995 Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by ``normal'' means. This schema implies, that error detection always comes
Menzel, Wolfgang
core +4 more sources