Multi-mode Natural Language Processing for human-robot interaction

2015 ◽  
Vol 13 (4) ◽  
pp. 267-278 ◽  
Author(s):  
Jiongkun Xie ◽  
Xiaoping Chen ◽  
Jianmin Ji
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Multi Mode

scholarly journals An Nlp-Based Approach for Improving Human-Robot Interaction

2013 ◽  
Vol 3 (3) ◽  
pp. 189-200 ◽  
Author(s):  
Yilmaz Kilicaslan ◽  
Gurkan Tuna
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Theoretical Basis ◽  
Human Robot Interaction ◽  
Semantic Representations ◽  
Grammatical Structure ◽  
Language Resources ◽  
Robot Interaction

Abstract This study aims to explore the possibility of improving human-robot interaction (HRI) by exploiting natural language resources and using natural language processing (NLP) methods. The theoretical basis of the study rests on the claim that effective and efficient human robot interaction requires linguistic and ontological agreement. A further claim is that the required ontology is implicitly present in the lexical and grammatical structure of natural language. The paper offers some NLP techniques to uncover (fragments of) the ontology hidden in natural language and to generate semantic representations of natural language sentences using that ontology. The paper also presents the implementation details of an NLP module capable of parsing English and Turkish along with an overview of the architecture of a robotic interface that makes use of this module for expressing the spatial motions of objects observed by a robot

scholarly journals Natural language instructions for human–robot collaborative manipulation

2018 ◽  
Vol 37 (6) ◽  
pp. 558-565 ◽  
Author(s):  
Rosario Scalise ◽  
Shen Li ◽  
Henny Admoni ◽  
Stephanie Rosenthal ◽  
Siddhartha S Srinivasa
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Human Robot Interaction ◽  
Robotic Manipulation ◽  
Easy Access ◽  
Domain Specific Language ◽  
Robot Interaction ◽  
Specific Language ◽  
Domain Specific ◽  
Object Reference

This paper presents a dataset of natural language instructions for object reference in manipulation scenarios. It comprises 1582 individual written instructions, which were collected via online crowdsourcing. This dataset is particularly useful for researchers who work in natural language processing, human–robot interaction, and robotic manipulation. In addition to serving as a rich corpus of domain-specific language, it provides a benchmark of image–instruction pairs to be used in system evaluations and uncovers inherent challenges in tabletop object specification. Example code is provided for easy access via Python.

scholarly journals Robots That Use Language

2020 ◽  
Vol 3 (1) ◽  
pp. 25-55
Author(s):  
Stefanie Tellex ◽  
Nakul Gopalan ◽  
Hadas Kress-Gazit ◽  
Cynthia Matuszek
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Language Use ◽  
Physical World ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Collaborative Dialogue ◽  
Sensors And Actuators ◽  
Robot Interaction ◽  
Human Partner

This article surveys the use of natural language in robotics from a robotics point of view. To use human language, robots must map words to aspects of the physical world, mediated by the robot's sensors and actuators. This problem differs from other natural language processing domains due to the need to ground the language to noisy percepts and physical actions. Here, we describe central aspects of language use by robots, including understanding natural language requests, using language to drive learning about the physical world, and engaging in collaborative dialogue with a human partner. We describe common approaches, roughly divided into learning methods, logic-based methods, and methods that focus on questions of human–robot interaction. Finally, we describe several application domains for language-using robots.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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