SlideShare une entreprise Scribd logo
1  sur  1
Télécharger pour lire hors ligne
Chinese Grammar VS English Grammar
in Universal Dependency
Hang Jiang, Jinho D. Choi, PhD
Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA 30322
The aims of UD
• Provide a concise generic set of features that are important to analyze different
languages.
• Annotate different corpus consistently across languages with some extensions on
some specific languages.
• This eventually makes parsing more accurate and easier
Status of UD
• 47 languages and dialects have had their own treebanks
• Chinese as the most spoken language of the world is excluded.
Project Goal
In this project, we are going to to compare the difference between Chinese and
English in UD in order to set up basic differences in building up Chinese treebank in
UD.
Motivation for UD
Different from people’s intuition, English and Chinese have similar basic grammar
that can be explained by Universal Grammar. English share similar structures with
Chinese in:
• Core dependents of clausal predicates (objects, subjects and complements but not
clausal complement)
• Root, coordination and loose joining
Those dependents and some other similar dependents basically show UD can be
applied to Chinese.
A good example can be shown below.
Fig.2 The graph representation shows that the Chinese and English sentences have highly corresponding dependency relation with each
other in many cases..
• The dependency relation is amazingly similar in a word-to-word level for both
Chinese and English sentences.
English Structures fit Chinese
English has many distinctive that features make us wonder whether English has
brought some extra UD relations to UD that other languages may not need.
English grammar show dramatic differences from that of Chinese mainly in (not
limited to):
• noun dependents (acl, det)
• non-core dependents of clausal predicates (nmod, advmod, neg)
• special clausal dependents (vocative, aux, mark, discourse, auxpass, expl)
• case markers (case).
Of expression is a good example.
Fig.3 An alternative way of saying ‘the weather office won’
• In English, of expression’s corresponding structure doesn’t exist in Chinese.
However, the regular noun modifiers are often followed by de (的), which is also a
case relation. So there still exists case relation in Chinese.
However, the following example is an exception found in our project.
Fig.4 The expletive it in English doesn’t exist in Chinese.
• The expletive it doesn't exist in Chinese at all. Instead Chinese has pro-dropping
and assumes the subject is weather in this context. However, it is still indisputable
that expl is necessary across languages.
• As a result, UD relation is considered very concise and generic after comparing
Chinese and English grammar.
English UD Examples unfit Chinese
Chinese has many different structural features compared with English. However, those
features are mainly distributed in (not limited to):
• noun classifiers
• prepositions, postpositions
• adjectives, comparatives
• aspect marker
• auxiliaries
Below are two Chinese examples with clear dependency relation.
1. The first example here is about consecutive verb use in Chinese.
Fig.5 Corresponding English to this example should be “He walks up (to somewhere).”
• The phenomenon of the consecutive use of verbs in Chinese can actually be treated
as asyndetic conjunction, which means the coordinating conjunction is omitted.
Chinese Structures Missing in UD examples
2. The use of prepositions and postpositions in Chinese
Fig. 6 The sentence means that “At school, I am always criticized.”
• 在(at) and 里(inside) are respectively preposition and postposition in Chinese.
Nevertheless, Ba sentence is the exception and we have to assign an ambiguous dep to
it. See the example in Fig.7.
Fig.7 English translationis that “It was I that let John finish and check homework for one time.”
• In this SOV ba sentence, it is not possible to treat ba as a preposition and assign a
case relation to ba and John(约翰) because every word can only have one head in
dependency relation. As a result, the isolated ba has to be dep related to the verb
following ba.
Contributions
• Show that UD is robust and basically compatible with Chinese
• Find out that ba sentence as a counterexample that Chinese doesn’t fit UD relation
• Provide clear relations, instead of dep, to Chinese distinctive structures in order to
better adapt UD to Chinese compared with Stanford parser
Future Work
• Explore in more details how UD can be adapted to fit Chinese by adapting
universal features and POS tags to Chinese morphology
• Build up a comprehensive guideline for Chinese UD and then construct Chinese
UD treebank.
Contributions and Future Work
Reference
• Choi, Jinho D., and Martha Palmer. Guidelines for the Clear style constituent to
dependency conversion. Technical Report 01-12, University of Colorado at
Boulder, 2012.
• De Marneffe, Marie-Catherine, and Christopher D. Manning. "The Stanford typed
dependencies representation." Coling 2008: Proceedings of the workshop on Cross-
Framework and Cross-Domain Parser Evaluation. Association for Computational
Linguistics, 2008.
• McDonald, Ryan T., et al. "Universal Dependency Annotation for Multilingual
Parsing." ACL (2). 2013.
Acknowledgement
• This research was supported by Emory NLP in terms of its assistance with Emory
NLP demo. See http://nlp.mathcs.emory.edu/.
Reference & Acknowledgement
English Spanish French Hindi Arabic
Tokens # 254K 423K 389K 351K 282K
Sentences
#
16K 16K 16K 16K 7K
Fig.1 The size of UD structures for some languages
UD (Universal Dependency) is an annotation scheme for multilingual dependency
structures, providing universal grammar.
• Dependency relation is a linguistic relation discussing mainly the notions of
subject, object, clausal complement, noun modifier, noun determiner and so on.
• Therefore, UD has a set of syntactic rules to label relations of words by
dependency relations.
Introduction

Contenu connexe

Tendances

Using Corpus Linguistics to Teach ESL Pronunication
Using Corpus Linguistics to Teach ESL PronunicationUsing Corpus Linguistics to Teach ESL Pronunication
Using Corpus Linguistics to Teach ESL PronunicationRebecca Allen
 
semantic ambiguity final
semantic ambiguity finalsemantic ambiguity final
semantic ambiguity finalRuby Slabicki
 
The locality principle- kiran
The locality principle- kiranThe locality principle- kiran
The locality principle- kirankiran nazir
 
NLP_KASHK:Context-Free Grammar for English
NLP_KASHK:Context-Free Grammar for EnglishNLP_KASHK:Context-Free Grammar for English
NLP_KASHK:Context-Free Grammar for EnglishHemantha Kulathilake
 
Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...
Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...
Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...Yukino Ikegami
 
Principles and parameters of grammar report
Principles and parameters of grammar reportPrinciples and parameters of grammar report
Principles and parameters of grammar reportAubrey Expressionista
 
Descriptive vs prescriptive
Descriptive vs prescriptiveDescriptive vs prescriptive
Descriptive vs prescriptiveRehan Baloch
 
M1 lesson 2.2 slides
M1 lesson 2.2 slidesM1 lesson 2.2 slides
M1 lesson 2.2 slidesAnh Le
 
Parise concordance feedback poster 2014_電気通信大学_final2
Parise concordance feedback poster 2014_電気通信大学_final2Parise concordance feedback poster 2014_電気通信大学_final2
Parise concordance feedback poster 2014_電気通信大学_final2Peter Parise
 
NLP_KASHK:Parsing with Context-Free Grammar
NLP_KASHK:Parsing with Context-Free Grammar NLP_KASHK:Parsing with Context-Free Grammar
NLP_KASHK:Parsing with Context-Free Grammar Hemantha Kulathilake
 
Minimalist program
Minimalist programMinimalist program
Minimalist programRabbiaAzam
 
Generative grammar
Generative grammarGenerative grammar
Generative grammarLheo Fronda
 
NLP_KASHK:Finite-State Morphological Parsing
NLP_KASHK:Finite-State Morphological ParsingNLP_KASHK:Finite-State Morphological Parsing
NLP_KASHK:Finite-State Morphological ParsingHemantha Kulathilake
 
Does the verb come last in your languages
Does the verb come last in your languagesDoes the verb come last in your languages
Does the verb come last in your languagesSamRobert9
 

Tendances (20)

Using Corpus Linguistics to Teach ESL Pronunication
Using Corpus Linguistics to Teach ESL PronunicationUsing Corpus Linguistics to Teach ESL Pronunication
Using Corpus Linguistics to Teach ESL Pronunication
 
semantic ambiguity final
semantic ambiguity finalsemantic ambiguity final
semantic ambiguity final
 
The locality principle- kiran
The locality principle- kiranThe locality principle- kiran
The locality principle- kiran
 
NLP_KASHK:Morphology
NLP_KASHK:MorphologyNLP_KASHK:Morphology
NLP_KASHK:Morphology
 
NLP_KASHK:Context-Free Grammar for English
NLP_KASHK:Context-Free Grammar for EnglishNLP_KASHK:Context-Free Grammar for English
NLP_KASHK:Context-Free Grammar for English
 
Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...
Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...
Clause Anaphora Resolution for Japanese Demonstrative Determiner based on Sem...
 
Principles and parameters of grammar report
Principles and parameters of grammar reportPrinciples and parameters of grammar report
Principles and parameters of grammar report
 
Descriptive vs prescriptive
Descriptive vs prescriptiveDescriptive vs prescriptive
Descriptive vs prescriptive
 
Syntax
SyntaxSyntax
Syntax
 
Syntax suuliinh
Syntax suuliinhSyntax suuliinh
Syntax suuliinh
 
M1 lesson 2.2 slides
M1 lesson 2.2 slidesM1 lesson 2.2 slides
M1 lesson 2.2 slides
 
Parise concordance feedback poster 2014_電気通信大学_final2
Parise concordance feedback poster 2014_電気通信大学_final2Parise concordance feedback poster 2014_電気通信大学_final2
Parise concordance feedback poster 2014_電気通信大学_final2
 
Chapter 2 syntax
Chapter 2 syntaxChapter 2 syntax
Chapter 2 syntax
 
Binding theory
Binding theoryBinding theory
Binding theory
 
NLP_KASHK:Parsing with Context-Free Grammar
NLP_KASHK:Parsing with Context-Free Grammar NLP_KASHK:Parsing with Context-Free Grammar
NLP_KASHK:Parsing with Context-Free Grammar
 
Minimalist program
Minimalist programMinimalist program
Minimalist program
 
Generative grammar
Generative grammarGenerative grammar
Generative grammar
 
Syntax lecture
Syntax lectureSyntax lecture
Syntax lecture
 
NLP_KASHK:Finite-State Morphological Parsing
NLP_KASHK:Finite-State Morphological ParsingNLP_KASHK:Finite-State Morphological Parsing
NLP_KASHK:Finite-State Morphological Parsing
 
Does the verb come last in your languages
Does the verb come last in your languagesDoes the verb come last in your languages
Does the verb come last in your languages
 

Similaire à Chinese Grammar vs English Grammar in Universal Dependency

Relative clause with their equivalence from English into Indonesian
Relative clause with their equivalence from English into IndonesianRelative clause with their equivalence from English into Indonesian
Relative clause with their equivalence from English into Indonesianimadejuliarta
 
美国教授对中国学生写英文文章的建议
美国教授对中国学生写英文文章的建议美国教授对中国学生写英文文章的建议
美国教授对中国学生写英文文章的建议chengcheng zhou
 
June2010 feedback How to tackle the yr 13 Language Exam
June2010 feedback How to tackle the yr 13 Language ExamJune2010 feedback How to tackle the yr 13 Language Exam
June2010 feedback How to tackle the yr 13 Language Examsteddyss
 
Semantics session 7_8_11_2021 Logic.pdf
Semantics session 7_8_11_2021 Logic.pdfSemantics session 7_8_11_2021 Logic.pdf
Semantics session 7_8_11_2021 Logic.pdfDr.Badriya Al Mamari
 
A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...
A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...
A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...Sun Waltre
 
Coordination Conjunction Errors in Written English Made by First Year Student...
Coordination Conjunction Errors in Written English Made by First Year Student...Coordination Conjunction Errors in Written English Made by First Year Student...
Coordination Conjunction Errors in Written English Made by First Year Student...ijtsrd
 
Basic sentence structure
Basic sentence structureBasic sentence structure
Basic sentence structurehalima888
 
Basic sentence structure
Basic sentence structureBasic sentence structure
Basic sentence structurehalima888
 
NS-CUK Seminar: J.H.Lee, Review on "Abstract Meaning Representation for Semb...
NS-CUK Seminar: J.H.Lee,  Review on "Abstract Meaning Representation for Semb...NS-CUK Seminar: J.H.Lee,  Review on "Abstract Meaning Representation for Semb...
NS-CUK Seminar: J.H.Lee, Review on "Abstract Meaning Representation for Semb...ssuser4b1f48
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Saurabh Kaushik
 
Applied Linguistics "Grammar"
Applied Linguistics "Grammar"Applied Linguistics "Grammar"
Applied Linguistics "Grammar"Kum Visal
 
grammar project NRA clauses
grammar project NRA clausesgrammar project NRA clauses
grammar project NRA clausesJoseph Emerson
 
A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]WriteKraft Dissertations
 
A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors  [www.writekraft.com]A syntactic account of some errors  [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]WriteKraft Dissertations
 
A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]WriteKraft Dissertations
 

Similaire à Chinese Grammar vs English Grammar in Universal Dependency (20)

Kupt.ppd.tutors.manual
Kupt.ppd.tutors.manualKupt.ppd.tutors.manual
Kupt.ppd.tutors.manual
 
Relative clause with their equivalence from English into Indonesian
Relative clause with their equivalence from English into IndonesianRelative clause with their equivalence from English into Indonesian
Relative clause with their equivalence from English into Indonesian
 
美国教授对中国学生写英文文章的建议
美国教授对中国学生写英文文章的建议美国教授对中国学生写英文文章的建议
美国教授对中国学生写英文文章的建议
 
Discourse
Discourse Discourse
Discourse
 
June2010 feedback How to tackle the yr 13 Language Exam
June2010 feedback How to tackle the yr 13 Language ExamJune2010 feedback How to tackle the yr 13 Language Exam
June2010 feedback How to tackle the yr 13 Language Exam
 
Syntax
SyntaxSyntax
Syntax
 
Semantics session 7_8_11_2021 Logic.pdf
Semantics session 7_8_11_2021 Logic.pdfSemantics session 7_8_11_2021 Logic.pdf
Semantics session 7_8_11_2021 Logic.pdf
 
A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...
A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...
A Student's Introduction To English Grammar by Huddleston Rodney & Pullum Geo...
 
Coordination Conjunction Errors in Written English Made by First Year Student...
Coordination Conjunction Errors in Written English Made by First Year Student...Coordination Conjunction Errors in Written English Made by First Year Student...
Coordination Conjunction Errors in Written English Made by First Year Student...
 
Basic sentence structure
Basic sentence structureBasic sentence structure
Basic sentence structure
 
Basic sentence structure
Basic sentence structureBasic sentence structure
Basic sentence structure
 
NS-CUK Seminar: J.H.Lee, Review on "Abstract Meaning Representation for Semb...
NS-CUK Seminar: J.H.Lee,  Review on "Abstract Meaning Representation for Semb...NS-CUK Seminar: J.H.Lee,  Review on "Abstract Meaning Representation for Semb...
NS-CUK Seminar: J.H.Lee, Review on "Abstract Meaning Representation for Semb...
 
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1Engineering Intelligent NLP Applications Using Deep Learning – Part 1
Engineering Intelligent NLP Applications Using Deep Learning – Part 1
 
Applied Linguistics "Grammar"
Applied Linguistics "Grammar"Applied Linguistics "Grammar"
Applied Linguistics "Grammar"
 
L4995100.pdf
L4995100.pdfL4995100.pdf
L4995100.pdf
 
grammar project NRA clauses
grammar project NRA clausesgrammar project NRA clauses
grammar project NRA clauses
 
A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]
 
A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors  [www.writekraft.com]A syntactic account of some errors  [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]
 
A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]A syntactic account of some errors [www.writekraft.com]
A syntactic account of some errors [www.writekraft.com]
 
Esp.language descriptions
Esp.language descriptionsEsp.language descriptions
Esp.language descriptions
 

Plus de Jinho Choi

Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...Jinho Choi
 
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...Jinho Choi
 
Competence-Level Prediction and Resume & Job Description Matching Using Conte...
Competence-Level Prediction and Resume & Job Description Matching Using Conte...Competence-Level Prediction and Resume & Job Description Matching Using Conte...
Competence-Level Prediction and Resume & Job Description Matching Using Conte...Jinho Choi
 
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...Jinho Choi
 
The Myth of Higher-Order Inference in Coreference Resolution
The Myth of Higher-Order Inference in Coreference ResolutionThe Myth of Higher-Order Inference in Coreference Resolution
The Myth of Higher-Order Inference in Coreference ResolutionJinho Choi
 
Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...
Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...
Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...Jinho Choi
 
Abstract Meaning Representation
Abstract Meaning RepresentationAbstract Meaning Representation
Abstract Meaning RepresentationJinho Choi
 
Semantic Role Labeling
Semantic Role LabelingSemantic Role Labeling
Semantic Role LabelingJinho Choi
 
CS329 - WordNet Similarities
CS329 - WordNet SimilaritiesCS329 - WordNet Similarities
CS329 - WordNet SimilaritiesJinho Choi
 
CS329 - Lexical Relations
CS329 - Lexical RelationsCS329 - Lexical Relations
CS329 - Lexical RelationsJinho Choi
 
Automatic Knowledge Base Expansion for Dialogue Management
Automatic Knowledge Base Expansion for Dialogue ManagementAutomatic Knowledge Base Expansion for Dialogue Management
Automatic Knowledge Base Expansion for Dialogue ManagementJinho Choi
 
Attention is All You Need for AMR Parsing
Attention is All You Need for AMR ParsingAttention is All You Need for AMR Parsing
Attention is All You Need for AMR ParsingJinho Choi
 
Graph-to-Text Generation and its Applications to Dialogue
Graph-to-Text Generation and its Applications to DialogueGraph-to-Text Generation and its Applications to Dialogue
Graph-to-Text Generation and its Applications to DialogueJinho Choi
 
Real-time Coreference Resolution for Dialogue Understanding
Real-time Coreference Resolution for Dialogue UnderstandingReal-time Coreference Resolution for Dialogue Understanding
Real-time Coreference Resolution for Dialogue UnderstandingJinho Choi
 
Topological Sort
Topological SortTopological Sort
Topological SortJinho Choi
 
Multi-modal Embedding Learning for Early Detection of Alzheimer's Disease
Multi-modal Embedding Learning for Early Detection of Alzheimer's DiseaseMulti-modal Embedding Learning for Early Detection of Alzheimer's Disease
Multi-modal Embedding Learning for Early Detection of Alzheimer's DiseaseJinho Choi
 
Building Widely-Interpretable Semantic Networks for Dialogue Contexts
Building Widely-Interpretable Semantic Networks for Dialogue ContextsBuilding Widely-Interpretable Semantic Networks for Dialogue Contexts
Building Widely-Interpretable Semantic Networks for Dialogue ContextsJinho Choi
 
How to make Emora talk about Sports Intelligently
How to make Emora talk about Sports IntelligentlyHow to make Emora talk about Sports Intelligently
How to make Emora talk about Sports IntelligentlyJinho Choi
 

Plus de Jinho Choi (20)

Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
 
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP D...
 
Competence-Level Prediction and Resume & Job Description Matching Using Conte...
Competence-Level Prediction and Resume & Job Description Matching Using Conte...Competence-Level Prediction and Resume & Job Description Matching Using Conte...
Competence-Level Prediction and Resume & Job Description Matching Using Conte...
 
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-b...
 
The Myth of Higher-Order Inference in Coreference Resolution
The Myth of Higher-Order Inference in Coreference ResolutionThe Myth of Higher-Order Inference in Coreference Resolution
The Myth of Higher-Order Inference in Coreference Resolution
 
Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...
Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...
Noise Pollution in Hospital Readmission Prediction: Long Document Classificat...
 
Abstract Meaning Representation
Abstract Meaning RepresentationAbstract Meaning Representation
Abstract Meaning Representation
 
Semantic Role Labeling
Semantic Role LabelingSemantic Role Labeling
Semantic Role Labeling
 
CKY Parsing
CKY ParsingCKY Parsing
CKY Parsing
 
CS329 - WordNet Similarities
CS329 - WordNet SimilaritiesCS329 - WordNet Similarities
CS329 - WordNet Similarities
 
CS329 - Lexical Relations
CS329 - Lexical RelationsCS329 - Lexical Relations
CS329 - Lexical Relations
 
Automatic Knowledge Base Expansion for Dialogue Management
Automatic Knowledge Base Expansion for Dialogue ManagementAutomatic Knowledge Base Expansion for Dialogue Management
Automatic Knowledge Base Expansion for Dialogue Management
 
Attention is All You Need for AMR Parsing
Attention is All You Need for AMR ParsingAttention is All You Need for AMR Parsing
Attention is All You Need for AMR Parsing
 
Graph-to-Text Generation and its Applications to Dialogue
Graph-to-Text Generation and its Applications to DialogueGraph-to-Text Generation and its Applications to Dialogue
Graph-to-Text Generation and its Applications to Dialogue
 
Real-time Coreference Resolution for Dialogue Understanding
Real-time Coreference Resolution for Dialogue UnderstandingReal-time Coreference Resolution for Dialogue Understanding
Real-time Coreference Resolution for Dialogue Understanding
 
Topological Sort
Topological SortTopological Sort
Topological Sort
 
Tries - Put
Tries - PutTries - Put
Tries - Put
 
Multi-modal Embedding Learning for Early Detection of Alzheimer's Disease
Multi-modal Embedding Learning for Early Detection of Alzheimer's DiseaseMulti-modal Embedding Learning for Early Detection of Alzheimer's Disease
Multi-modal Embedding Learning for Early Detection of Alzheimer's Disease
 
Building Widely-Interpretable Semantic Networks for Dialogue Contexts
Building Widely-Interpretable Semantic Networks for Dialogue ContextsBuilding Widely-Interpretable Semantic Networks for Dialogue Contexts
Building Widely-Interpretable Semantic Networks for Dialogue Contexts
 
How to make Emora talk about Sports Intelligently
How to make Emora talk about Sports IntelligentlyHow to make Emora talk about Sports Intelligently
How to make Emora talk about Sports Intelligently
 

Dernier

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEarley Information Science
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking MenDelhi Call girls
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?Antenna Manufacturer Coco
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?Igalia
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 

Dernier (20)

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 

Chinese Grammar vs English Grammar in Universal Dependency

  • 1. Chinese Grammar VS English Grammar in Universal Dependency Hang Jiang, Jinho D. Choi, PhD Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA 30322 The aims of UD • Provide a concise generic set of features that are important to analyze different languages. • Annotate different corpus consistently across languages with some extensions on some specific languages. • This eventually makes parsing more accurate and easier Status of UD • 47 languages and dialects have had their own treebanks • Chinese as the most spoken language of the world is excluded. Project Goal In this project, we are going to to compare the difference between Chinese and English in UD in order to set up basic differences in building up Chinese treebank in UD. Motivation for UD Different from people’s intuition, English and Chinese have similar basic grammar that can be explained by Universal Grammar. English share similar structures with Chinese in: • Core dependents of clausal predicates (objects, subjects and complements but not clausal complement) • Root, coordination and loose joining Those dependents and some other similar dependents basically show UD can be applied to Chinese. A good example can be shown below. Fig.2 The graph representation shows that the Chinese and English sentences have highly corresponding dependency relation with each other in many cases.. • The dependency relation is amazingly similar in a word-to-word level for both Chinese and English sentences. English Structures fit Chinese English has many distinctive that features make us wonder whether English has brought some extra UD relations to UD that other languages may not need. English grammar show dramatic differences from that of Chinese mainly in (not limited to): • noun dependents (acl, det) • non-core dependents of clausal predicates (nmod, advmod, neg) • special clausal dependents (vocative, aux, mark, discourse, auxpass, expl) • case markers (case). Of expression is a good example. Fig.3 An alternative way of saying ‘the weather office won’ • In English, of expression’s corresponding structure doesn’t exist in Chinese. However, the regular noun modifiers are often followed by de (的), which is also a case relation. So there still exists case relation in Chinese. However, the following example is an exception found in our project. Fig.4 The expletive it in English doesn’t exist in Chinese. • The expletive it doesn't exist in Chinese at all. Instead Chinese has pro-dropping and assumes the subject is weather in this context. However, it is still indisputable that expl is necessary across languages. • As a result, UD relation is considered very concise and generic after comparing Chinese and English grammar. English UD Examples unfit Chinese Chinese has many different structural features compared with English. However, those features are mainly distributed in (not limited to): • noun classifiers • prepositions, postpositions • adjectives, comparatives • aspect marker • auxiliaries Below are two Chinese examples with clear dependency relation. 1. The first example here is about consecutive verb use in Chinese. Fig.5 Corresponding English to this example should be “He walks up (to somewhere).” • The phenomenon of the consecutive use of verbs in Chinese can actually be treated as asyndetic conjunction, which means the coordinating conjunction is omitted. Chinese Structures Missing in UD examples 2. The use of prepositions and postpositions in Chinese Fig. 6 The sentence means that “At school, I am always criticized.” • 在(at) and 里(inside) are respectively preposition and postposition in Chinese. Nevertheless, Ba sentence is the exception and we have to assign an ambiguous dep to it. See the example in Fig.7. Fig.7 English translationis that “It was I that let John finish and check homework for one time.” • In this SOV ba sentence, it is not possible to treat ba as a preposition and assign a case relation to ba and John(约翰) because every word can only have one head in dependency relation. As a result, the isolated ba has to be dep related to the verb following ba. Contributions • Show that UD is robust and basically compatible with Chinese • Find out that ba sentence as a counterexample that Chinese doesn’t fit UD relation • Provide clear relations, instead of dep, to Chinese distinctive structures in order to better adapt UD to Chinese compared with Stanford parser Future Work • Explore in more details how UD can be adapted to fit Chinese by adapting universal features and POS tags to Chinese morphology • Build up a comprehensive guideline for Chinese UD and then construct Chinese UD treebank. Contributions and Future Work Reference • Choi, Jinho D., and Martha Palmer. Guidelines for the Clear style constituent to dependency conversion. Technical Report 01-12, University of Colorado at Boulder, 2012. • De Marneffe, Marie-Catherine, and Christopher D. Manning. "The Stanford typed dependencies representation." Coling 2008: Proceedings of the workshop on Cross- Framework and Cross-Domain Parser Evaluation. Association for Computational Linguistics, 2008. • McDonald, Ryan T., et al. "Universal Dependency Annotation for Multilingual Parsing." ACL (2). 2013. Acknowledgement • This research was supported by Emory NLP in terms of its assistance with Emory NLP demo. See http://nlp.mathcs.emory.edu/. Reference & Acknowledgement English Spanish French Hindi Arabic Tokens # 254K 423K 389K 351K 282K Sentences # 16K 16K 16K 16K 7K Fig.1 The size of UD structures for some languages UD (Universal Dependency) is an annotation scheme for multilingual dependency structures, providing universal grammar. • Dependency relation is a linguistic relation discussing mainly the notions of subject, object, clausal complement, noun modifier, noun determiner and so on. • Therefore, UD has a set of syntactic rules to label relations of words by dependency relations. Introduction