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Time-implicit Hierarchies in Different Languages ...
Anonymous. - : Zenodo, 2021
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Time-implicit Hierarchies in Different Languages ...
Anonymous. - : Zenodo, 2021
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3
Time-implicit Hierarchies in Different Languages ...
Anonymous. - : Zenodo, 2021
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4
Time-implicit Hierarchies in Different Languages ...
Anonymous. - : Zenodo, 2021
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5
Sources Matter: A Comparison of Fake News Datasets on Linguistic Feature Performance ...
Wang, Miaohan. - : University of Chicago, 2021
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6
Language and Reasoning by Entropy Fractals
In: Signals ; Volume 2 ; Issue 4 ; Pages 44-770 (2021)
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The Cultural Outcomes of Social Movements: A Computational Linguistics Approach
In: Partecipazione e conflitto; Vol. 14, No. 3 (2021). Special Issue on: "When, where and which kind of collective action matters?"; 1151-1179 (2021)
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8
Une approche computationnelle de la complexité linguistique par le traitement automatique du langage naturel et l'oculométrie
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9
Living Machines atypical animacy dataset ...
: British Library, 2020
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10
Machine learning methods for vector-based compositional semantics ...
Maillard, Jean. - : Apollo - University of Cambridge Repository, 2020
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11
Learning meaning representations for text generation with deep generative models ...
Cao, Kris. - : Apollo - University of Cambridge Repository, 2020
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12
Modelling speaker adaptation in second language learner dialogue ...
Sinclair, Arabella Jane. - : The University of Edinburgh, 2020
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13
The LiLa Activity at Linked Pasts 6 ...
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14
The LiLa Activity at Linked Pasts 6 ...
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15
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances ...
Weigelt, Sebastian; Steurer, Vanessa; Hey, Tobias; Tichy, Walter F.. - : World Scientific Publishing, 2020
Abstract: Systems with conversational interfaces are rather popular nowadays. However, their full potential is not yet exploited. For the time being, users are restricted to calling predefined functions. Soon, users will expect to customize systems to their needs and create own functions using nothing but spoken instructions. Thus, future systems must understand how laypersons teach new functionality to intelligent systems. The understanding of natural language teaching sequences is a first step toward comprehensive end-user programming in natural language. We propose to analyze the semantics of spoken teaching sequences with a hierarchical classification approach. First, we classify whether an utterance constitutes an effort to teach a new function or not. Afterward, a second classifier locates the distinct semantic parts of teaching efforts: declaration of a new function, specification of intermediate steps, and superfluous information. For both tasks we implement a broad range of machine learning techniques: ...
Keyword: artificial intelligence; computational linguistics; conversational interfaces; end-user programming; intelligent systems; machine learning; natural language processing; natural language understanding; naturalistic programming; neural networks; Programming in natural language; spoken language understanding
URL: https://dx.doi.org/10.5445/ir/1000124405/post
https://publikationen.bibliothek.kit.edu/1000124405/post
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16
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances
In: International journal of semantic computing, 14 (2), 249–272 ; ISSN: 1793-351X, 1793-7108 (2020)
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17
Modelling speaker adaptation in second language learner dialogue
Sinclair, Arabella Jane. - : The University of Edinburgh, 2020
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18
Sentence Simplification for Text Processing
Evans, Richard. - : University of Wolverhampton, 2020
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19
Defining distinctiveness: A computational and experimental analysis
Spear, Jackie. - 2020
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20
Learning to Parse Grounded Language using Reservoir Computing
In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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