scholarly journals Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity

2020 ◽  
Author(s):  
Terufumi Morishita ◽  
Gaku Morio ◽  
Hiroaki Ozaki ◽  
Toshinori Miyoshi
Keyword(s):  
Word Similarity ◽  
Representation Spaces ◽  
Human Sense

A hard choice for Tomasello

2020 ◽  
Vol 43 ◽  
Author(s):  
Philip Pettit
Keyword(s):  
Joint Action ◽  
The Other ◽  
Hard Choice ◽  
Margaret Gilbert ◽  
Michael Bratman ◽  
Human Sense ◽  
Sense Of Obligation

Abstract Michael Tomasello explains the human sense of obligation by the role it plays in negotiating practices of acting jointly and the commitments they underwrite. He draws in his work on two models of joint action, one from Michael Bratman, the other from Margaret Gilbert. But Bratman's makes the explanation too difficult to succeed, and Gilbert's makes it too easy.

The role of cross-linguistic stress pattern frequency and word similarity on the acquisition of English stress pattern by native speakers of Brazilian Portuguese

2012 ◽  
Vol 12 ◽  
Author(s):  
Amanda Post Silveira
Keyword(s):  
Second Language ◽  
Native Speakers ◽  
Mother Tongue ◽  
American English ◽  
Brazilian Portuguese ◽  
Stress Pattern ◽  
Target Language ◽  
English As Second Language ◽  
Word Similarity ◽  
L1 Transfer

This is a preliminary study in which we investigate the acquisition of English as second language (L2[1]) word stress by native speakers of Brazilian Portuguese (BP, L1[2]). In this paper, we show results of a multiple choice forced choice perception test in which native speakers of American English and native speakers of Dutch judged the production of English words bearing pre-final stress that were both cognates and non-cognates with BP words. The tokens were produced by native speakers of American English and by Brazilians that speak English as a second language. The results have shown that American and Dutch listeners were consistent in their judgments on native and non-native stress productions and both speakers' groups produced variation in stress in relation to the canonical pattern. However, the variability found in American English points to the prosodic patterns of English and the variability found in Brazilian English points to the stress patterns of Portuguese. It occurs especially in words whose forms activate neighboring similar words in the L1. Transfer from the L1 appears both at segmental and prosodic levels in BP English. [1] L2 stands for second language, foreign language, target language. [2] L1 stands for first language, mother tongue, source language.

Analysis Accuracy of Similar Word Based Clustering (EWSB) Algorithm on Machine Translator Bahasa Indonesia-Minang

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Herry Sujaini
Keyword(s):  
Machine Translation ◽  
Clustering Algorithm ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Word Similarity ◽  
Similar Word ◽  
Word Clustering ◽  
Translation Accuracy ◽  
Bahasa Indonesia

Extended Word Similarity Based (EWSB) Clustering is a word clustering algorithm based on the value of words similarity obtained from the computation of a corpus. One of the benefits of clustering with this algorithm is to improve the translation of a statistical machine translation. Previous research proved that EWSB algorithm could improve the Indonesian-English translator, where the algorithm was applied to Indonesian language as target language.This paper discusses the results of a research using EWSB algorithm on a Indonesian to Minang statistical machine translator, where the algorithm is applied to Minang language as the target language. The research obtained resulted that the EWSB algorithm is quite effective when used in Minang language as the target language. The results of this study indicate that EWSB algorithm can improve the translation accuracy by 6.36%.

scholarly journals Design and Calibration of a Force/Tactile Sensor for Dexterous Manipulation

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 966 ◽  
Author(s):  
Marco Costanzo ◽  
Giuseppe De Maria ◽  
Ciro Natale ◽  
Salvatore Pirozzi
Keyword(s):  
Experimental Validation ◽  
Sensory System ◽  
Tactile Sensor ◽  
Contact Forces ◽  
Dexterous Manipulation ◽  
Soft Contact ◽  
Grasping Force ◽  
Sense Of Touch ◽  
Human Sense ◽  
Robotic Applications

This paper presents the design and calibration of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping tasks, especially when objects are fragile, such that only a minimum amount of grasping force can be applied to hold the object without damaging it. Moreover, sensing only forces and not moments can be very limiting to securely grasp an object when it is grasped far from its center of gravity. Therefore, the perception of torsional moments is a key requirement of the designed sensor. Furthermore, the sensor is also the mechanical interface between the gripper and the manipulated object, therefore its design should consider also the requirements for a correct holding of the object. The most relevant of such requirements is the necessity to hold a torsional moment, therefore a soft distributed contact is necessary. The presence of a soft contact poses a number of challenges in the calibration of the sensor, and that is another contribution of this work. Experimental validation is provided in real grasping tasks with two sensors mounted on an industrial gripper.

scholarly journals Effects of COVID-19 on Sense of Smell: Human Factors/Ergonomics Considerations

2021 ◽  
pp. 001872082199016
Author(s):  
E. Leslie Cameron ◽  
Per Møller ◽  
Keith S. Karn
Keyword(s):  
Human Factors ◽  
Olfactory Dysfunction ◽  
System Communication ◽  
Human System ◽  
Sense Of Smell ◽  
Primary Means ◽  
Human Sense ◽  
Smell Loss

Objective We review the effects of COVID-19 on the human sense of smell (olfaction) and discuss implications for human-system interactions. We emphasize how critical smell is and how the widespread loss of smell due to COVID-19 will impact human-system interaction. Background COVID-19 reduces the sense of smell in people who contract the disease. Thus far, olfaction has received relatively little attention from human factors/ergonomics professionals. While smell is not a primary means of human-system communication, humans rely on smell in many important ways related to both quality of life and safety. Method We briefly review and synthesize the rapidly expanding literature through September 2020 on the topic of smell loss caused by COVID-19. We interpret findings in terms of their relevance to human factors/ergonomics researchers and practitioners. Results Since March 2020 dozens of articles have been published that report smell loss in COVID-19 patients. The prevalence and duration of COVID-19-related smell loss is still under investigation, but the available data suggest that it may leave many people with long-term deficits and distortions in sense of smell. Conclusion We suggest that the human factors/ergonomics community could become more aware of the importance of the sense of smell and focus on accommodating the increasing number of people with reduced olfactory performance. Application We present examples of how olfaction can augment human-system communication and how human factors/ergonomics professionals might accommodate people with olfactory dysfunction. While seemingly at odds, both of these goals can be achieved.

Cyberbullying Detection, Based on the FastText and Word Similarity Schemes

2020 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Kun Wang ◽  
Yanpeng Cui ◽  
Jianwei Hu ◽  
Yu Zhang ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Word Similarity ◽  
Cyberbullying Detection

Intelligent recognition of semantic relationships based on antonymy

2020 ◽  
Vol 16 (3) ◽  
pp. 263-290
Author(s):  
Hui Guan ◽  
Chengzhen Jia ◽  
Hongji Yang
Keyword(s):  
Semantic Similarity ◽  
New Approach ◽  
Word Similarity ◽  
Semantic Relationships ◽  
Proposed Model ◽  
Path Distance ◽  
The Hierarchical Structure ◽  
Thinking Process ◽  
Similarity Measuring ◽  
Intelligent Recognition

Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.

scholarly journals Multichannel acoustic source and image dataset for the cocktail party effect in hearing aid and implant users

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tim Fischer ◽  
Marco Caversaccio ◽  
Wilhelm Wimmer
Keyword(s):  
Hearing Aids ◽  
Scientific Community ◽  
Cocktail Party ◽  
Acoustic Source ◽  
Image Dataset ◽  
Cocktail Party Effect ◽  
Spatial Coordinates ◽  
Acoustic Chamber ◽  
Human Hearing ◽  
Human Sense

AbstractThe Cocktail Party Effect refers to the ability of the human sense of hearing to extract a specific target sound source from a mixture of background noises in complex acoustic scenarios. The ease with which normal hearing people perform this challenging task is in stark contrast to the difficulties that hearing-impaired subjects face in these situations. To help patients with hearing aids and implants, scientists are trying to imitate this ability of human hearing, with modest success so far. To support the scientific community in its efforts, we provide the Bern Cocktail Party (BCP) dataset consisting of 55938 Cocktail Party scenarios recorded from 20 people and a head and torso simulator wearing cochlear implant audio processors. The data were collected in an acoustic chamber with 16 synchronized microphones placed at purposeful positions on the participants’ heads. In addition to the multi-channel audio source and image recordings, the spatial coordinates of the microphone positions were digitized for each participant. Python scripts were provided to facilitate data processing.

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