Natural Language Text Retrieval Using a Large Semantic Network

1993 ◽  
Author(s):  
Paul Nelson
Keyword(s):  
Natural Language ◽  
Semantic Network ◽  
Text Retrieval ◽  
Natural Language Text ◽  
Language Text

HYBRID HEURISTIC SEMANTIC NETWORKS IN NATURAL LANGUAGE UNDERSTANDING

1995 ◽  
Vol 03 (04) ◽  
pp. 389-400 ◽  
Author(s):  
DAN CORBETT
Keyword(s):  
Natural Language ◽  
Semantic Network ◽  
Semantic Networks ◽  
Hybrid Network ◽  
Language Understanding ◽  
English Sentence ◽  
Hybrid Heuristic ◽  
Natural Language Text ◽  
Working Together ◽  
Language Text

It has never been demonstrated that a pure semantic analysts of an English sentence can be accomplished without any aid from a syntactic analyzer. It has therefore become interesting to demonstrate semantic systems which can be guided by fast and efficient syntactic methods. We show that non-probabilistic, abductive techniques can be used in a hybrid network to correctly interpret the meaning of an English sentence. We discuss the implementation of an abductive system which uses heuristics working together with a semantic network in an attempt to eliminate uncertainty and ambiguity in natural language text.

scholarly journals A Practical q -Gram Index for Text Retrieval Allowing Errors

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Gonzalo Navarro ◽  
Ricardo Baeza-Yates
Keyword(s):  
Natural Language ◽  
Text Retrieval ◽  
Search Pattern ◽  
Natural Language Text ◽  
Time Space ◽  
On Line ◽  
Text Searching ◽  
Space Requirements ◽  
Indexing Technique ◽  
Language Text

We propose an indexing technique for approximate text searching, which is practical and powerful, and especially optimized for natural language text. Unlike other indices of this kind, it is able to retrieve any string that approximately matches the search pattern, not only words. Every text substring of a fixed length q is stored in the index, together with pointers to all the text positions where it appears. The search pattern is partitioned into pieces which are searched in the index, and all their occurrences in the text are verified for a complete match. To reduce space requirements, pointers to blocks instead of exact positions can be used, which increases querying costs. We design an algorithm to optimize the pattern partition into pieces so that the total number of verifications is minimized. This is especially well suited for natural language texts, and allows to know in advance the expected cost of the search and the expected relevance of the query to the user. We show experimentally the building time, space requirements and querying time of our index, finding that it is a practical alternative for text retrieval. The retrieval times are reduced from 10% to 60% of the best on-line algorithm. 

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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