Comparative Analytical Study of Cutting-Edge Dependency Parsing for Nature Language Processing

2018 ◽  
pp. 1239-1244
Author(s):  
Han He ◽  
Lei Wu ◽  
Hua Yan ◽  
Yi Feng
Keyword(s):  
Language Processing ◽  
Analytical Study ◽  
Cutting Edge ◽  
Dependency Parsing ◽  
Nature Language Processing

Multi-level Chunk-based Constituent-to-Dependency Treebank Transformation for Tibetan Dependency Parsing

2021 ◽  
Vol 20 (2) ◽  
pp. 1-12
Author(s):  
Shumin Shi ◽  
Dan Luo ◽  
Xing Wu ◽  
Congjun Long ◽  
Heyan Huang
Keyword(s):  
Language Processing ◽  
Manual Annotation ◽  
Syntactic Parsing ◽  
Dependency Parsing ◽  
Low Resource ◽  
Resource Setting ◽  
Dependency Tree ◽  
Low Resource Setting ◽  
Novel Method ◽  
Multi Level

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research on the construction of treebank. We propose a novel method of multi-level chunk-based syntactic parsing to complete constituent-to-dependency treebank conversion for Tibetan under scarce conditions. Our method mines more dependencies of Tibetan sentences, builds a high-quality Tibetan dependency tree corpus, and makes fuller use of the inherent laws of the language itself. We train the dependency parsing models on the dependency treebank obtained by the preliminary transformation. The model achieves 86.5% accuracy, 96% LAS, and 97.85% UAS, which exceeds the optimal results of existing conversion methods. The experimental results show that our method has the potential to use a low-resource setting, which means we not only solve the problem of scarce Tibetan dependency treebank but also avoid needless manual annotation. The method embodies the regularity of strong knowledge-guided linguistic analysis methods, which is of great significance to promote the research of Tibetan information processing.

scholarly journals Identificação de Pragas e Doenças na Cultura da Soja por meio de um Sistema Computacional em Linguagem Natural

2021 ◽  
Author(s):  
Carolinne Roque e Faria ◽  
Cinthyan Renata Sachs Camerlengo de Barb
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer System ◽  
Language Processing ◽  
Agricultural Area ◽  
Syntactic Analysis ◽  
Dependency Parsing ◽  
Named Entities ◽  
Pests And Diseases ◽  
Improve Production

Technology is becoming expressively popular among agribusiness producers and is progressing in all agricultural area. One of the difficulties in this context is to handle data in natural language to solve problems in the field of agriculture. In order to build up dialogs and provide rich researchers, the present work uses Natural Language Processing (NLP) techniques to develop an automatic and effective computer system to interact with the user and assist in the identification of pests and diseases in the soybean farming, stored in a database repository to provide accurate diagnoses to simplify the work of the agricultural professional and also for those who deal with a lot of information in this area. Information on 108 pests and 19 diseases that damage Brazilian soybean was collected from Brazilian bibliographic manuals with the purpose to optimize the data and improve production, using the spaCy library for syntactic analysis of NLP, which allowed the pre-process the texts, recognize the named entities, calculate the similarity between the words, verify dependency parsing and also provided the support for the development requirements of the CAROLINA tool (Robotized Agronomic Conversation in Natural Language) using the language belonging to the agricultural area.

scholarly journals Multi-attribute Recognition,The key to Universal Neural Network

2021 ◽  
Author(s):  
Jinxin Wei
Keyword(s):  
Neural Network ◽  
Continuous Function ◽  
Language Processing ◽  
Deep Neural Network ◽  
Input Node ◽  
Output Node ◽  
Nature Language Processing ◽  
Generation Network ◽  
Universal Network ◽  
Attribute Recognition

<p><b>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly.</b><b> </b><b>The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The </b><b>c</b><b>oncrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs.</b><b> I guess that t</b><b>he phenomenon of synaesthesia is the result of multi-input and multi-output. </b><b>I guess that c</b><b>onnection in mind can realize through the universal network and sending the output into input.</b><b></b></p>

scholarly journals Critique on Cache Transition Techniques for Semantic Graph Parsing for optimizing Search Process using Text Mining

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1014-1018
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Vital Role ◽  
Transition System ◽  
Search Process ◽  
Dependency Parsing ◽  
Fixed Size ◽  
Decomposition Theory

This paper elaborates the transition system that gives the standard transition-based dependency parsing techniques for generating the graph. It is essential to know the standard transition techniques for all graphical problems. Cache transition technique plays a vital role in optimizing the search process in various text mining applications. This paper provides an overview on cache transition technique for parsing semantic graphs for several Natural Language Processing (NLP) applications. According to this paper, the cache is having the fixed size m, by tree decomposition theory according to which there is a relationship between the parameter m and class of graphs produced by the theory.

Dependency parsing of Polish

2019 ◽  
Vol 55 (2) ◽  
pp. 305-337 ◽  
Author(s):  
Alina Wróblewska ◽  
Piotr Rybak
Keyword(s):  
Language Processing ◽  
Argument Structure ◽  
Question Answering ◽  
State Of The Art ◽  
Dependency Parsing ◽  
Dependency Theory ◽  
Crucial Issue ◽  
Experimental Part ◽  
Predicate Argument Structure

Abstract The predicate-argument structure transparently encoded in dependency-based syntactic representations supports machine translation, question answering, information extraction, etc. The quality of dependency parsing is therefore a crucial issue in natural language processing. In the current paper we discuss the fundamental ideas of the dependency theory and provide an overview of selected dependency-based resources for Polish. Furthermore, we present some state-of-the-art dependency parsing systems whose models can be estimated on correctly annotated data. In the experimental part, we provide an in-depth evaluation of these systems on Polish data. Our results show that graph-based parsers, even those without any neural component, are better suited for Polish than transition-based parsing systems.

Extensions to constraint dependency parsing for spoken language processing

1995 ◽  
Vol 9 (3) ◽  
pp. 187-234 ◽  
Author(s):  
Mary P. Harper ◽  
Randall A. Helzerman
Keyword(s):  
Language Processing ◽  
Spoken Language ◽  
Dependency Parsing ◽  
Spoken Language Processing

Bimodal bilingualism: Factors yet to be explored

2015 ◽  
Vol 19 (2) ◽  
pp. 259-260 ◽  
Author(s):  
GLADYS TANG
Keyword(s):  
Language Processing ◽  
Cognitive Skills ◽  
Cutting Edge ◽  
Neural Systems ◽  
Cognitive Effects ◽  
Underlying Assumption ◽  
Knowledge Representations

Recent years have seen increasing research into bimodal bilingualism from a variety of paradigms such as bilingual acquisition, language processing, neural systems, and cognitive skills, with the underlying assumption that successful bimodal bilingualism entails the knowledge representations and processing of two grammars each of which via a distinct modality, auditory-oral versus visual-gestural. As such, it opens up an arena of cutting-edge research enabling comparisons of the linguistic and cognitive effects of monolingualism versus bilingualism, as well as unimodal bilingualism versus bimodal bilingualism.

scholarly journals Multi-attribute Recognition,the key to Universal Neural Network

2021 ◽  
Author(s):  
Jinxin Wei
Keyword(s):  
Neural Network ◽  
Continuous Function ◽  
Language Processing ◽  
Deep Neural Network ◽  
Input Node ◽  
Output Node ◽  
Nature Language Processing ◽  
Generation Network ◽  
Universal Network ◽  
Attribute Recognition

<p>To achieve the recognition of multi-attribute of object, I redesign the mnist dataset, change the color, size, location of the number. Meanwhile, I change the label accordingly. The deep neural network I use is the most common convolution neural network. Through test,we can conclude that we can use one neural network to recognize multi-attribute so long as the attribute difference of objects can be represented by functions. The concrete network(generation network) can generate the output which the input rarely contained from the attributes the network learned. Its generalization ability is good because the network is a continuous function. Through one more test, We can conclude that one neural network can do image recognition, speech recognition,and nature language processing and other things so long as the output node and the input node and more parameters add into the network. The network is universal so long as the network can process different inputs. I guess that the phenomenon of synaesthesia is the result of multi-input and multi-output. I guess that connection in mind can realize through the universal network and sending the output into input.<b></b></p>

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