| 1 |
A Comparative Review on Applications of Different Sensors for Sign Language Recognition
|
|
|
|
In: Journal of Imaging; Volume 8; Issue 4; Pages: 98 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Controlled Generation of Stylized Text Using Semantic and Phonetic Representations
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
ANLIzing the Adversarial Natural Language Inference Dataset
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2022)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Improving Reader Motivation with Machine Learning
|
|
|
|
In: Electronic Thesis and Dissertation Repository (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Learning To Compositionally Reason Over Natural Language
|
|
|
|
In: Publicly Accessible Penn Dissertations (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Introduction
|
|
|
|
In: Comment parle un robot ? Les machines à langage dans la science-fiction ; https://hal.archives-ouvertes.fr/hal-02533441 ; Comment parle un robot ? Les machines à langage dans la science-fiction, Le Bélial', pp. 21-43, 2020, Collection Parallaxe, 978-2-84344-965-9 ; https://www.belial.fr/ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
Comment parle un robot ? ; Comment parle un robot ?: Les machines à langage dans la science-fiction
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-02548113 ; Le Bélial', 2020, Collection Parallaxe, 978-2-84344-965-9 ; https://www.belial.fr/ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Learning Natural Language from Probabilistic Perceptual Representations with Limited Resources ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Language-Driven Region Pointer Advancement for Controllable Image Captioning
|
|
|
|
In: Conference papers (2020)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
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)
|
|
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: classical approaches, such as Naïve Bayes, and neural network configurations of various types and architectures, such as bidirectional LSTMs. Additionally, we introduce two heuristic-based adaptations that are tailored to the task of understanding teaching sequences. As data basis we use 3168 descriptions gathered in a user study. For the first task convolutional neural networks obtain the best results (accuracy: 96.6%); bidirectional LSTMs excel in the second (accuracy: 98.8%). The adaptations improve the first-level classification considerably (plus 2.2% points).
|
|
Keyword:
artificial intelligence; computational linguistics; conversational interfaces; DATA processing & computer science; ddc:004; end-user programming; info:eu-repo/classification/ddc/004; intelligent systems; machine learning; natural language processing; natural language understanding; naturalistic programming; neural networks; Programming in natural language; spoken language understanding
|
|
URL: https://publikationen.bibliothek.kit.edu/1000124405
https://doi.org/10.5445/IR/1000124405/post
https://publikationen.bibliothek.kit.edu/1000124405/130160664
|
|
BASE
|
|
Hide details
|
|
|
| 14 |
Understanding Natural Language with Commonsense Knowledge Representation, Reasoning, and Simulation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Automatic and Adaptative Emojis Recommendation ; Recommandation automatique et adaptative d'emojis
|
|
|
|
In: https://hal-amu.archives-ouvertes.fr/tel-02491135 ; Informatique et langage [cs.CL]. Aix-Marseille Université, 2019. Français (2019)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Multilingual Fake News Detection with Satire
|
|
|
|
In: CICLing: International Conference on Computational Linguistics and Intelligent Text Processing ; https://halshs.archives-ouvertes.fr/halshs-02391141 ; CICLing: International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France (2019)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Analysis of scientific production based on trending research topics. An Artificial Intelligence case study
|
|
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner
|
|
|
|
In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.archives-ouvertes.fr/hal-01888694 ; Cognition, Elsevier, 2018, 173, pp.43 - 59. ⟨10.1016/j.cognition.2017.11.008⟩ (2018)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Boosting Supervised Neural Relation Extraction with Distant Supervision
|
|
|
|
In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1524095334803486 (2018)
|
|
BASE
|
|
Show details
|
|
|
|
|
