In Search of a Human Language Processing System

1985 ◽  
Vol 30 (7) ◽  
pp. 529-531
Author(s):  
Patrick Carroll
Keyword(s):  
Language Processing ◽  
Processing System ◽  
Human Language

scholarly journals Discovering prominence and its role in language processing: An individual (differences) approach

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Phillip M. Alday ◽  
Matthias Schlesewsky ◽  
Ina Bornkessel-Schlesewsky
Keyword(s):  
Individual Differences ◽  
Real Time ◽  
Language Processing ◽  
Language Comprehension ◽  
Processing System ◽  
Partial Ordering ◽  
Human Language ◽  
Qualitative Work ◽  
State Of Affairs ◽  
The Relationship

AbstractIt has been suggested that, during real time language comprehension, the human language processing system attempts to identify the argument primarily responsible for the state of affairs (the “actor”) as quickly and unambiguously as possible. However, previous work on a prominence (e.g. animacy, definiteness, case marking) based heuristic for actor identification has suffered from underspecification of the relationship between different cue hierarchies. Qualitative work has yielded a partial ordering of many features (e.g.: OpenSesame experiment and Python support scripts, sample stimuli, R scripts for analysis

scholarly journals A Review of Natural Language Processing Techniques: Application to Afan Oromo

2021 ◽  
Vol 10 (03) ◽  
pp. 051-054
Author(s):  
Bekele Abera Hordofa ◽  
Shambel Dechasa Degefa
Keyword(s):  
National Identity ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing System ◽  
Writing System ◽  
Human Language ◽  
Natural Language Processing System ◽  
Current Trends ◽  
Processing Techniques

Language is a means of communication and a symbol of national identity. Afan Oromo is one of written and spoken indigenous language in Ethiopia which uses a writing system called Qubee. Natural language processing is automatic or semi-automatic processing of human language that helps computers to understand and process language. NLP techniques involve various linguistic levels to understand and use language. Linguistic levels are an explanatory method for presenting what actually happens within a natural language processing system. This is very important to develop appropriate and desired NLP applications at both higher and lower levels. In this paper, we present a review of techniques, current trends and challenges in NLP application to Afan Oromo.

EXPRESS: Selective adaptation in sentence comprehension: Evidence from event-related brain potentials

2020 ◽  
pp. 174702182098462
Author(s):  
Masataka Yano ◽  
Shugo Suwazono ◽  
Hiroshi Arao ◽  
Daichi Yasunaga ◽  
Hiroaki Oishi
Keyword(s):  
Language Processing ◽  
Sentence Comprehension ◽  
Processing System ◽  
Selective Adaptation ◽  
Event Related Potential ◽  
Equal Probability ◽  
N400 Effect ◽  
Mixed Effect ◽  
Processing Cost ◽  
Low Probability

The present study conducted two event-related potential experiments to investigate whether readers adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous sentences when they are repeatedly exposed to them. To address this issue, we manipulated the probability of morphosyntactically/semantically grammatical and anomalous sentence occurrence through experiments. For the low probability block, anomalous sentences were presented less frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effect models were used to examine how the size of the P600 effect changed as the experiment went along. The results showed that the smaller P600 effect of the equal probability block resulted from an amplitude’s decline in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect than their semantically natural counterparts regardless of probability manipulation. No evidence was found in favor of adaptation to semantic violations in that the processing cost of semantic violations did not decrease over the course of the experiment. Therefore, the present study demonstrated a dynamic aspect of language-processing system. We will discuss why the language-processing system shows a selective adaptation to morphosyntactic violations.

scholarly journals Sentence Generation for Indian Sign Language Using NLP

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 196-210
Author(s):  
Dr.P. Golda Jeyasheeli ◽  
N. Indumathi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Sign Language ◽  
Language Processing ◽  
Normal Person ◽  
Human Language ◽  
Android Application ◽  
Sentence Generation ◽  
Normal People ◽  
Indian Sign Language

Nowadays the interaction among deaf and mute people and normal people is difficult, because normal people scuffle to understand the sense of the gestures. The deaf and dumb people find problem in sentence formation and grammatical correction. To alleviate the issues faced by these people, an automatic sign language sentence generation approach is propounded. In this project, Natural Language Processing (NLP) based methods are used. NLP is a powerful tool for translation in the human language and also responsible for the formation of meaningful sentences from sign language symbols which is also understood by the normal person. In this system, both conventional NLP methods and Deep learning NLP methods are used for sentence generation. The efficiency of both the methods are compared. The generated sentence is displayed in the android application as an output. This system aims to connect the gap in the interaction among the deaf and dumb people and the normal people.

scholarly journals A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications

2020 ◽  
Vol 34 (08) ◽  
pp. 13369-13381
Author(s):  
Shivashankar Subramanian ◽  
Ioana Baldini ◽  
Sushma Ravichandran ◽  
Dmitriy A. Katz-Rogozhnikov ◽  
Karthikeyan Natesan Ramamurthy ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Generic Drugs ◽  
Low Cost ◽  
Processing System ◽  
Drug Repurposing ◽  
Cancer Type ◽  
Entity Extraction ◽  
Scientific Publications

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.

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