scholarly journals exture Oriented Scene Generation from Natural Language Text Description using 3D Coloured Objects

2021 ◽  
Vol 9 (VI) ◽  
pp. 520-526
Author(s):  
Yashaswini S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language ◽  
3D Modeling ◽  
Natural Language Semantics ◽  
Natural Language Text ◽  
Interesting Pattern ◽  
Language Semantics ◽  
The World ◽  
Language Text

To understand language, we need an understanding of the world around us. Language describes the world and provides symbols with which we represent meaning. Still, much knowledge about the world is so obvious that it is rarely explicitly stated. It is uncommon for people to state that chairs are usually on the floor and upright, and that you usually eat a cake from a plate on a table. Knowledge of such common facts provides the context within which people communicate with language. Therefore, to create practical systems that can interact with the world and communicate with people, we need to leverage such knowledge to interpret language in context. Scene generation can be used to achieve an ability to generate 3D scenes on basis of text description. A model capable of learning natural language semantics or interesting pattern to generate abstract idea behind scene composition is interesting [1].Scene generation from text involves several fields like NLP, artificial intelligence, computer vision and machine learning. This paper focuses on optimally arranging objects in a room with focus on the orientation of the objects with respect to the floor, wall and ceiling of a room along with textures. Our model suggest a novel framework which can be used as a tool to generate scene where anyone without 3D Modeling.

scholarly journals Form-Independent Meaning Representation for Eventualities

2019 ◽  
pp. 604-623
Author(s):  
Mark Steedman
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Semantic Representation ◽  
Logical Form ◽  
The Internet ◽  
Conceptual Representation ◽  
Natural Language Semantics ◽  
Language Semantics ◽  
Representation Language ◽  
Meaning Representation

Linguists and philosophers since Aristotle have attempted to reduce natural language semantics in general, and the semantics of eventualities in particular, to a ‘language of mind’, expressed in terms of various collections of underlying language-independent primitive concepts. While such systems have proved insightful enough to suggest that such a universal conceptual representation is in some sense psychologically real, the primitive relations proposed, based on oppositions like agent-patient, event-state, etc., have remained incompletely convincing. This chapter proposes that the primitive concepts of the language of mind are ‘hidden’, or latent, and must be discovered automatically by detecting consistent patterns of entailment in the vast amounts of text that are made available by the internet using automatic syntactic parsers and machine learning to mine a form- and language-independent semantic representation language for natural language semantics. The representations involved combine a distributional representation of ambiguity with a language of logical form.

Semantics as Model-Based Science

2018 ◽  
Author(s):  
Seth Yalcin
Keyword(s):  
Natural Language ◽  
Semantic Theory ◽  
Linguistic Competence ◽  
Natural Language Semantics ◽  
Theoretical Understanding ◽  
Model Based ◽  
Language Semantics ◽  
The World

This chapter critiques a number of standard ways of understanding the role of the metalanguage in a semantic theory for natural language, including the idea that disquotation plays a nontrivial role in any explanatory natural language semantics. It then proposes that the best way to understand the role of a semantic metalanguage involves recognizing that semantics is a model-based science. The metalanguage of semantics is language for articulating features of the theorist’s model. Models are understood as mediating instruments—idealized structures used to represent select aspects of the world, aspects the theorist is seeking some theoretical understanding of. The aspect of reality we are seeking some understanding of in semantics is a dimension of human linguistic competence—informally, knowledge of meaning.

scholarly journals How Players Speak to an Intelligent Game Character Using Natural Language Messages

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Fraser Allison ◽  
Ewa Luger ◽  
Katja Hofmann
Keyword(s):  
Machine Learning ◽  
High School Students ◽  
Natural Language ◽  
Design Space ◽  
Digital Games ◽  
Writing Style ◽  
School Students ◽  
Wizard Of Oz ◽  
Natural Language Text ◽  
Language Text

AI-driven characters that learn directly from human input are rare in digital games, but recent advances in several fields of machine learning suggests that they may soon be much more feasible to create. This study explores the design space for interacting with such a character through natural language text dialogue. We conducted an observational study with 18 high school students, who played Minecraft alongside a Wizard of Oz prototype of a companion AI character that learned from their actions and inputs. In this paper, we report on an analysis of the 186 natural language messages that players sent to the character, and review key variations in syntax, function and writing style. We find that players’ behaviour and language was differentiated by the extent to which they expressed an anthropomorphic view of the AI character and the level of interest that they showed in interacting with it.

scholarly journals An Efficient Approach to Restructure Natural Language Text

2021 ◽  
Vol 23 (07) ◽  
pp. 1445-1452
Author(s):  
Dr. R. N. Kulkarni ◽  
◽  
Swetha Koduri ◽  
Keyword(s):  
Software Development ◽  
Natural Language ◽  
English Language ◽  
Design Elements ◽  
Natural Language Text ◽  
Efficient Approach ◽  
Input Text ◽  
The World ◽  
Automated Tool ◽  
Language Text

During the recent times, there is enormous growth found in the domain of software development across the world. Many organizations are automating all the activities in the organization and for the development of any customized application; the developing organization needs to gather the requirements from the client organization. The gathered requirements may be structured or unstructured because of the flexibility and ambiguity in the English language. The natural language sometimes has the problem of flexibility because of which the same term may be written in a different way. To overcome the problems of natural language statements, in this paper an automated tool is proposed to restructure the sentences, paragraphs, simple statements, compound statements and pages which are grammatically correct. This approach takes the input text and converts compound statements into simple statements, removing duplicate statements, places one statement per line and assigns sequence numbers to each physical statement. This enables us to extract the correct and complete meaning of the statements for further processing and output achieved is used for abstraction of design elements.

scholarly journals Food Recommendation using Classifier and Modified Apriori Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 3967-3970
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Food Analysis ◽  
Apriori Algorithm ◽  
Computer Technologies ◽  
The World

In today’s world, computer technologies have advanced a lot. One of its greatest gifts to the world is Artificial Intelligence. Natural Language Processing (NLP) and Machine Learning (ML) are two of its subdomains. In this paper, modified versions of two common NLP and ML algorithms have been used to classify food reviews and provide suitable recommendations from them. Currently, reviews can be classified into positive and negative reviews, but it becomes difficult when one review says positive about item A and negative about item B. Moreover, the current Apriori algorithm doesn’t consider the feedbacks from customers (reviews). Modified classifier algorithm and consequently, modified Apriori algorithm has been used to classify each statement part by part and provide recommendations, not just on previous purchases but also using the reviews about above-mentioned purchases. The algorithms can be used for purposes other than food analysis also – wherever purchases and reviews are involved. For e.g., e-commerce companies can use the algorithms to predict and recommend suitable items a user may be interested in.

scholarly journals Latest Advances of Natural Language Processing and their Applications in Everyday life

2020 ◽  
Vol 6 (10) ◽  
pp. 31-35
Author(s):  
Sai Sri Nandan Challapalli Shalini Jaiswal and Preeti Singh Bahadur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Constraint Satisfaction ◽  
Large Scale ◽  
The World ◽  
Dramatic Shift

Natural language processing (NLP) area of Artificial Intelligence (AI) has offered the scope to apply and integrate various other traditional AI fields. While the world was working on comparatively simpler aspects like constraint satisfaction and logical reasoning, the last decade saw a dramatic shift in the research. Now large-scale applications of statistical methods, such as machine learning and data mining are in the limelight. At the same time, the integration of this understanding with Computer Vision, a tech that deals with obtaining information from visual data through cameras will pave way to bring the AI enabled devices closer to a layman also. This paper gives an overview of implementation and trend analysis of such technology in Sales and ServiceSectors.

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