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Text Understanding from Scratch
[article]
2016
arXiv
pre-print
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This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). ...
We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. ...
To decide on the first question, we extract all replaceable words from the given text and randomly choose r of them to be replaced. ...
arXiv:1502.01710v5
fatcat:f4s7ymfranckfjerey2snotcvu
Multidisciplinary Approaches to Text Understanding
1982
Contemporary Psychology
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In these books they discuss text understanding from the perspectives of linguistics, psychology, philosophy, and artificial intelligence.
Each book examines text understand- ing at a different level. ...
Multidisciplinary Approaches to Text Understanding
Robert de Beaugrande
Text, Discourse, and Process: Toward a Multidisciplinary Science of Texts. ...
doi:10.1037/020596
fatcat:o2rau2pb2raljam6wjmqlcrsgy
Natural Scene Text Understanding
[chapter]
2007
Vision Systems: Segmentation and Pattern Recognition
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In particular, natural scene text understanding aiming at extracting text from daily images is the main concern of this text. ...
NS text understanding may help to properly extract text from backgrounds to apply a particular compression to scene text, as already forecast in DjVu. ...
doi:10.5772/4966
fatcat:2vx67sdtrzfpfousqoagdukutm
The KERNEL text understanding system
1993
Artificial Intelligence
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This article describes KERNEL, a text understanding system developed at the Unisys Center for Advanced Information Technology. ...
It has stressed the encoding of deep, general common sense 43 PROTEUS is an acronym for PROtotype TExt Understanding System. the kernel text understanding system 43 and domain knowledge as predicate ...
All conceptual predicates represented in the knowledge base are indicated here by the a x -P. the kernel text understanding system 23 includesP period34 , moment34 in 13a. 30 13 a. ...
doi:10.1016/0004-3702(93)90014-3
fatcat:jdxpictivzfofedwia6aw5ly3y
A Framework for Procedural Text Understanding
2015
Proceedings of the 14th International Conference on Parsing Technologies
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In this paper we propose a framework for procedural text understanding. ...
the concepts in the text at once. ...
We extract features from labeled arcs (u, v, l) by two processes: first we extract features from the arc and input recipe text and then we concatenate the label l to each feature we extract. ...
doi:10.18653/v1/w15-2206
dblp:conf/iwpt/MaetaSM15
fatcat:f6i6ql5wrrbzde7jfeukz4n4s4
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which leverages external knowledge sources to solve these issues. ...
Specifically, we retrieve informative knowledge triples from ConceptNet and perform knowledge-aware reasoning while tracking the entities. ...
To sum up, we introduce our KnOwledge-Aware proceduraL text understAnding (KOALA) model, which incorporates commonsense knowledge from ConceptNet and is trained with a multi-stage schema. ...
arXiv:2009.13199v1
fatcat:dbh3yip5mvdpvim2hjoohm6eoi
Device-Dependent Readability for Improved Text Understanding
2014
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
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Readability is used to provide users with highquality service in text recommendation or text visualization. ...
They are extracted from 28 French Foreign Language (FFL) textbooks written for adults learning FFL. ...
The texts written for adults actually contain more entities than those written for children (Barzilay and Lapata, 2008) . The same is true for VP. ...
doi:10.3115/v1/d14-1146
dblp:conf/emnlp/KimSPL14
fatcat:dyx7n5z4nrethiltevbaqjp55y
Contextual Text Understanding in Distributional Semantic Space
2015
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15
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Representing discrete words in a continuous vector space turns out to be useful for natural language applications related to text understanding. ...
We demonstrate the effectiveness of the framework in a number of tasks on text understanding, including word/phrase similarity measurements, paraphrase identification and question-answer relatedness classification ...
In all experiments related to text understanding, including contextual word/phrase similarity measurements, paraphrase identification and question-answer relatedness classification. we achieve impressive ...
doi:10.1145/2806416.2806517
dblp:conf/cikm/ChengWWYC15
fatcat:f7f6nsonn5h77bnpmppvnxqeuq
Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation
2021
Applied Sciences
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This study proposes a new logical model for intelligent software architecture devoted to improving the efficiency of automated text understanding and text generation in industrial applications. ...
Data Availability Statement: The data presented in this study are available on request from the corresponding author upon reasonable request. ...
from texts formed in natural languages. ...
doi:10.3390/app11115179
fatcat:i2ln6dgdmfgblkfk3jam7czwl4
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
[article]
2022
arXiv
pre-print
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We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. ...
However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of ...
The right candidates are extracted from the original stories (at the position of "[MASK]") while the wrong candidates are written by crowd-sourced annotators. ...
arXiv:2108.12960v2
fatcat:6c4g5rhwureftcoao2d5tah6wa
Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling
2018
The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18
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2018 pre-print arXiv:1805.01334v1 fatcat:gl573x24nzdozgerhr3ta4ssga
We also provide examples showing how KESM conveys its text understanding ability learned from entity salience to search. ...
This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. ...
ranking features convey KESM's text understanding ability learned from entity salience labels to search. ...
doi:10.1145/3209978.3209982
dblp:conf/sigir/XiongLCL18
fatcat:52w27kyxlnavle7xgdjr4xy4re
Simulating Text Understanding for Educational Applications with Latent Semantic Analysis: Introduction to LSA
2000
Interactive Learning Environments
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The following seven articles describe new educational tools that rely on a unique capability of computer programs to abstract knowledge relationships from vast quantities of text, and using this, to determine ...
the similarity of knowledge expressed in two or more texts. ...
Cohen describes how Latent Semantic Indexing (LSI) was used to model expert knowledge of a complex domain using very short texts written by journeymen and experts alone, rather than from a broad knowledge ...
doi:10.1076/1049-4820(200008)8:2;1-b;ft073
fatcat:zxwbsb4vkvdjxnyhkt2ilxcdm4
Implementing of Reading Strategies in the Academic Process as the Productive Way of Foreign Text Understanding
2015
Mediterranean Journal of Social Sciences
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The authors of the paper put the relevant issue associated with a perception of foreign texts presented in English for pupils and students at schools and universities. ...
learning strategy" and "reading strategy" definitions the authors suggest the algorithm to be studied and applied in the academic process at schools and universities as the main way to understand authentic texts ...
Differential reading algorithm is aimed at the main information extraction and written compression of a text due to key words' isolating, semantic numbers' building and textual dominants' identification ...
doi:10.5901/mjss.2015.v6n6s4p390
fatcat:7fx4fxjeubc67klgh4dhqhmyva
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
2022
Transactions of the Association for Computational Linguistics
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We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. ...
However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of ...
The right candidates are extracted from the original stories (at the position of ''[MASK]'') while the wrong candidates are written by crowd-sourced annotators. ...
doi:10.1162/tacl_a_00469
fatcat:wzxedwqfnbawvo3gbgmo4cjvpi
A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide-Coverage Text Understanding
2011
Language and Linguistics Compass
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To make interesting inferences, often additional background knowledge is required (not expressed in the analysed text or speech parts). ...
This can be derived (and turned into first-order logic) from raw text, semistructured databases or large-scale lexical databases such as WordNet. ...
Extracting Semantic Relations from Text Instead of relying on manually crafted lexical resources, one could also calculate lexical knowledge on the basis of large corpora. ...
doi:10.1111/j.1749-818x.2011.00284.x
fatcat:23b6p3m2w5bi7llo6vi6hie24y
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