CONVERTING NATURAL LANGUAGE TEXT SENTENCES INTO SPN REPRESENTATIONS FOR ASSOCIATING EVENTS

2012 ◽  
Vol 06 (03) ◽  
pp. 353-370 ◽  
Author(s):  
NIKOLAOS BOURBAKIS ◽  
MICHAEL MILLS
Keyword(s):  
Natural Language ◽  
Graph Model ◽  
Stochastic Petri Nets ◽  
Natural Language Text ◽  
Multimodal Information ◽  
Efficient Processing ◽  
Functional Knowledge ◽  
Efficient Representation ◽  
Language Text ◽  
Selection Of

A better understanding of events many times requires the association and the efficient representation of multi-modal information. A good approach to this important issue is the development of a common platform for converting different modalities (such as images, text, etc.) into the same medium and associating them for efficient processing and understanding. In a previous paper we have presented a Local-Global graph model for the conversion of images into graphs with attributes and then into natural language (NL) text sentences [25]. Here, in this paper we propose the conversion of NL text sentences into graphs and then into Stochastic Petri-nets (SPN) descriptions in order to efficiently offer a model of associating "activities or changes" in multimodal information for events representation and understanding. The selection of the SPN graph model is due to its capability for efficiently representing structural and functional knowledge. Simple illustrative examples are provided for proving the concept proposed here.

Deep Understanding of Technical Documents: Part II. Automatic Extraction of Pseudocode

2021 ◽  
Vol 30 (03) ◽  
pp. 2150016
Author(s):  
N. G. Bourbakis ◽  
G. Rematska ◽  
S. Mertoguno
Keyword(s):  
Natural Language ◽  
Petri Nets ◽  
Deep Understanding ◽  
Stochastic Petri Nets ◽  
Automatic Extraction ◽  
Natural Language Text ◽  
Technical Documents ◽  
Language Text

Humans have the privilege to automatically have a deep understanding of technical documents, since they have the ability to deal with complex concepts coming from many different modalities, like diagrams, text, tables, formulas, graphics, pictures, etc. For many years researchers are working to transfer such potential to AI based machines. This paper takes the advantage of the synergistic and interactive enrichment of two TD modalities, the block diagrams and the associated natural language text, obtained to automatically generate pseudocode that describes the functionality of the system under study. The methodology for generating the code is mainly based on the mapping of the TD modalities into Stochastic Petri-nets (SPN) that enriches the system diagrams, from which the pseudocode is generated. The overall methodology will contribute to an automatic deep understanding of technical documents (TD) without the main involvement of humans. Two illustrative examples are also provided for describing the methodology.

Glossa — A Formal Language as a Mapping Mechanism of NL Sentences into SPN State Machine for Actions/Events Association

2017 ◽  
Vol 26 (02) ◽  
pp. 1750012 ◽  
Author(s):  
Adamantia Psarologou ◽  
Nikolaos Bourbakis
Keyword(s):  
Natural Language ◽  
Formal Language ◽  
Research Work ◽  
State Machine ◽  
Stochastic Petri Nets ◽  
Anaphora Resolution ◽  
Language Understanding ◽  
Natural Language Text ◽  
Mapping Mechanism ◽  
Language Text

Natural Language Understanding (NLU) is an old and really challenging field with a variety of research work published on it. In this paper we present a formal language methodology based on a state machine for efficiently representing natural language events/actions and their associations in well-written documents. The methodology consists of the following steps. We firstly apply Anaphora Resolution (AR) to the pre-processing natural language text. Then we extract the kernel(s) of each sentence. These kernels are formally represented using a formal language, (Glossa) to map the language expressions (kernels) into Stochastic Petri Nets (SPN) graphs. Finally we apply a set of rules to combine the SPN graphs in order to achieve the associations of actions/events in time. Special emphasis of this paper is the mapping of kernels of NL sentences into SPN graphs. Note that this work does not cover all the aspects of the NLU. Examples of SPN graphs of different NL texts, produced by our proposed methodology are given.

Morality Classification in Natural Language Text

2020 ◽  
pp. 1-1
Author(s):  
Matheus C. Pavan ◽  
Vitor G. Santos ◽  
Alex G. J. Lan ◽  
Joao Martins ◽  
Wesley Ramos Santos ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

scholarly journals Word-based self-indexes for natural language text

2012 ◽  
Vol 30 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Antonio Fariña ◽  
Nieves R. Brisaboa ◽  
Gonzalo Navarro ◽  
Francisco Claude ◽  
Ángeles S. Places ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Language Text

Wordform as the main basis for analysis of natural language text

2021 ◽  
pp. 101-108
Author(s):  
S.G. Antonov
Keyword(s):  
Natural Language ◽  
Natural Language Text ◽  
Probability Approach ◽  
Correction Problem ◽  
Main Basis ◽  
Language Corpus ◽  
Language Text

In the article discuss the application aspects of wordforms of natural language text for decision the mistakes correction problem. Discuss the merits and demerits of two known approaches for decision – deterministic and based on probabilities/ Construction principles of natural language corpus described, wich apply in probability approach. Declare conclusion about necessity of complex using these approaches in dependence on properties of texts.

A Review on Question Generation from Natural Language Text

2022 ◽  
Vol 40 (1) ◽  
pp. 1-43
Author(s):  
Ruqing Zhang ◽  
Jiafeng Guo ◽  
Lu Chen ◽  
Yixing Fan ◽  
Xueqi Cheng
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Augmentation ◽  
Text Structure ◽  
Current Status ◽  
Question Generation ◽  
Natural Language Text ◽  
Question Answering Systems ◽  
The Right ◽  
Language Text

Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.

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