C SC 620 Advanced Topics in Natural Language Processing Lecture 23 4/20.

Slides:



Advertisements
Similar presentations
PM—Propositional Model A Computational Psycholinguistic Model of Language Comprehension Based on a Relational Analysis of Written English Jerry T. Ball,
Advertisements

CSA4050: Advanced Topics in NLP Example Based MT.
Term 3 ____ Unit 9 Future holidays and activities, future school trip, Futuroscope, a story in French. ____ Unit 10 Visit to a safari park, football match,
Section 4: Language and Intelligence Overview Instructor: Sandiway Fong Department of Linguistics Department of Computer Science.
C SC 620 Advanced Topics in Natural Language Processing Lecture 22 4/15.
Machine Translation (Level 2) Anna Sågvall Hein GSLT Course, September 2004.
C SC 620 Advanced Topics in Natural Language Processing Lecture 20 4/8.
C SC 620 Advanced Topics in Natural Language Processing Lecture 21 4/13.
C SC 620 Advanced Topics in Natural Language Processing Lecture 8 2/12/04.
J. Turmo, 2006 Adaptive Information Extraction Summary Information Extraction Systems Multilinguality Introduction Language guessers Machine Translators.
C SC 620 Advanced Topics in Natural Language Processing Lecture 19 4/6.
Machine Translation Prof. Alexandros Potamianos Dept. of Electrical & Computer Engineering Technical University of Crete, Greece May 2003.
C SC 620 Advanced Topics in Natural Language Processing Lecture 24 4/22.
MT Summit VIII, Language Technologies Institute School of Computer Science Carnegie Mellon University Pre-processing of Bilingual Corpora for Mandarin-English.
Comments on Guillaume Pitel: “Using bilingual LSA for FrameNet annotation of French text from generic resources” Gerd Fliedner Computational Linguistics.
C SC 620 Advanced Topics in Natural Language Processing 3/9 Lecture 14.
C SC 620 Advanced Topics in Natural Language Processing Lecture 10 2/19.
C SC 620 Advanced Topics in Natural Language Processing Lecture 17 3/25.
The Relevance of Grammar Taking learners beyond ‘I watching TV’ Taking learners beyond ‘I watching TV’
3. Active vs. Passive Voice
Language Translators By: Henry Zaremba. Origins of Translator Technology ▫1954- IBM gives a demo of a translation program called the “Georgetown-IBM experiment”
Lecture 1, 7/21/2005Natural Language Processing1 CS60057 Speech &Natural Language Processing Autumn 2005 Lecture 1 21 July 2005.
Stages of Second Language Acquisition
The Big Picture – La Grande Image! What is involved in learning French in Year 9? In Year 9 you will build on the four skills of speaking, listening, reading.
Working with English Learners
Submitted By: Guriqubal Kaur ( English Teacher) G.S.S.S, Dhangrali (Ropar) Class:9 th Subject: English.
From paragraph to essay
Lecture 12: 22/6/1435 Natural language processing Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
Grammar Presentation & Practice
Chilton Pirates The Writing Process P. O. W. E. R.
CRESST ONR/NETC Meetings, July 2003, v1 ONR Advanced Distributed Learning Linguistic Modification of Test Items Jamal Abedi University of California,
Machine Translation  Machine translation is of one of the earliest uses of AI  Two approaches:  Traditional approach using grammars, rewrite rules,
Approaches to Machine Translation CSC 5930 Machine Translation Fall 2012 Dr. Tom Way.
Тема курсовой работы «Пассивный залог» Фёдорова В. А. Учитель английского языка Гимназия №343.
Because... Provides: English writing models. Vocabulary Grammar Punctuation Construct sentences Paragraphs Texts Real + Interest = Balance text SKIM SCAN.
How to teach grammar through texts LindaKen. Texts and Contexts What does this word mean? Can I have a word with you? I give you my word. Word has it.
1 And yeah, it was really good! Positive stance in native and learner speech Sylive De Cock Centre for English Corpus Linguistics Université catholique.
Machine Translation (Level 2) Anna Sågvall Hein GSLT Course, January 2003.
Rules, Movement, Ambiguity
Auckland 2012Kilgarriff: NLP and Corpus Processing1 The contribution of NLP: corpus processing.
Natural Language Processing Chapter 1 : Introduction.
Second Language Acquisition
Jan 2005CSA4050 Machine Translation II1 CSA4050: Advanced Techniques in NLP Machine Translation II Direct MT Transfer MT Interlingual MT.
February 1 st through 5 th English 4. Monday, February 1 st Snow Day.
Introduction: definition of a lesson plan It can be simple as a mental checklist or as a complex as a detailed two-page typed lesson plan.
 explain expected stages and patterns of language development as related to first and second language acquisition (critical period hypothesis– Proficiency.
Natural Language Processing Group Computer Sc. & Engg. Department JADAVPUR UNIVERSITY KOLKATA – , INDIA. Professor Sivaji Bandyopadhyay
HOW TO TACKLE AN ESSAY WITH THE WHAT 4 STRATEGY. GIVE YOUR ESSAY THE WHAT 4 BEFORE READING WHAT IS THE TOPIC? WHAT IS THE PROMPT ASKING YOU TO DO? WHAT.
HOW TO TACKLE AN ESSAY WITH THE WHAT 4 STRATEGY. GIVE YOUR ESSAY THE WHAT 4 BEFORE READING WHAT IS THE TOPIC? WHAT IS THE PROMPT ASKING YOU TO DO? WHAT.
A Simple English-to-Punjabi Translation System By : Shailendra Singh.
Jan 2012MT Architectures1 Human Language Technology Machine Translation Architectures Direct MT Transfer MT Interlingual MT.
LingWear Language Technology for the Information Warrior Alex Waibel, Lori Levin Alon Lavie, Robert Frederking Carnegie Mellon University.
Understanding unstructured texts via Latent Dirichlet Allocation Raphael Cohen DSaaS, EMC IT June 2015.
GGGE6533 LANGUAGE LEARNING STRATEGY INSTRUCTION SUCCESSFUL ENGLISH LANGUAGE LEARNING INVENTORY (SELL-IN) FINDINGS & IMPLICATIONS PREPARED BY: ZULAIKHA.
Correct Voice Active vs. passive
Approaches to Machine Translation
6 TIPS on ACTIVE AND PASSIVE VOICES
USERS’ PERCEPTIONS OF THE E-MENU PROTOTYPE ON E-MENU FEATURES
Natural Language Understanding
ICT and Writing Skills Lynn W Zimmerman, PhD.
Teaching Listening LLT 307!.
Parent/Student Writing Resource
BBI 3212 ENGLISH SYNTAX AND MORPHOLOGY
Add To Your Agenda: Vocabulary Words Graphic Organizer
Approaches to Machine Translation
C SC 620 Advanced Topics in Natural Language Processing
Helping English Learners Be Successful!
Word embeddings (continued)
Linguistic aspects of interlanguage
Writing 1: Parts of a written piece
Presentation transcript:

C SC 620 Advanced Topics in Natural Language Processing Lecture 23 4/20

Reading List Readings in Machine Translation, Eds. Nirenburg, S. et al. MIT Press –19. Montague Grammar and Machine Translation. Landsbergen, J. –20. Dialogue Translation vs. Text Translation – Interpretation Based Approach. Tsujii, J.-I. And M. Nagao –21. Translation by Structural Correspondences. Kaplan, R. et al. –22. Pros and Cons of the Pivot and Transfer Approaches in Multilingual Machine Translation. Boitet, C. –31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. –32. A Statistical Approach to Machine Translation. Brown, P. F. et al.

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Time: Early to mid-80s, took off in the 90s First cited paper in the popular Example-Based Machine Translation (EBMT) framework Prototypical Consideration –Motivated by experience of 2nd Language Learners –Starting from canned sentences/constructions in English/Japanese –Then proceeding to sentences that are slight variants of the initial examples –Case frame (ditransitive verbs) (English) S verb O C (Japanese) S’-topic O’-direct-object C’-indirect-object verb’ –Slow learning process, lots of data needed

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. A Modified Approach –Replacement strategy Use a thesaurus (e.g. WordNet) –Example Given –(English) A man eats vegetables –(Japanese) man-topic vegetable-object eat Find translation for –(English) He eats potatoes Correspondences –Man ~ he –Vegetable ~ potato Translation –(Japanese) he-topic potato-object eat

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. WordNet –Potato and Vegetable potato/n is in [potato,white_potato,Irish_potato,murphy,spud,tater] [potato,white_potato,Irish_potato,murphy,spud,tater] is an instance of [root_vegetable] [root_vegetable] is an instance of [vegetable,veggie] vegetable/n is in the synset [vegetable,veggie] –OK

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. WordNet –Man and he man/n is in [world,human_race,humanity,humankind,human_beings,humans,mankind,ma n] [world,human_race,humanity,humankind,human_beings,humans,mankind,ma n] is an instance of [group,grouping] [arrangement] is an instance of [group,grouping] [ordering,order,ordination] is an instance of [arrangement] [word_order] is an instance of [ordering,order,ordination] [word_order] is a part holonym of [text,textual_matter] [letter,missive] is an instance of [text,textual_matter] letter and letter/n related by noun.communication [he] is an instance of [letter,letter_of_the_alphabet,alphabetic_character] –No good

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Example 2 –(English) Acid eats metal –Correspondences Acid - man Metal - vegetable –WordNet acid/n is in [lysergic_acid_diethylamide,LSD,acid] [lysergic_acid_diethylamide,LSD,acid] is an instance of [drug_of_abuse,street_drug] [drug_of_abuse,street_drug] is an instance of [drug] [drug] is an instance of [agent] [agent] is an instance of [causal_agent,cause,causal_agency] [person,individual,someone,somebody,mortal,human,soul] is an instance of [causal_agent,cause,causal_agency] [man] is an instance of [person,individual,someone,somebody,mortal,human,soul]

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Example 2 –WordNet metal/n is in [alloy,metal] [alloy,metal] is an instance of [mixture] [mixture] is an instance of [substance,matter] [solid] is an instance of [substance,matter] [food] is an instance of [solid] [produce,green_goods,green_groceries,garden_truck] is an instance of [food] [vegetable,veggie] is an instance of [produce,green_goods,green_groceries,garden_truck] vegetable/n is in the synset [vegetable,veggie] –Different verb is used Okasu - {eat, invade, attack}

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Example 3 (Closest Match) –Given Same verb (yabureru) - be defeated/broken (Japanese) he-topic election-dative be defeated (English) He was defeated by the election (Japanese) paper bag-topic weight-by be broken (English) The paper bag was broken by the weight –Translate (Japanese) president-topic vote-dative yabureta Correspondences –President ~ man vs. paper bag –Vote ~ election vs. weight (English) The president was defeated by the vote

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Example 3 (Closest Match) –WordNet Man ~ president –president/n is in [president,chairman,chairwoman,chair,chairperson] –[president,chairman,chairwoman,chair,chairperson] is an instance of [presiding_officer] –[presiding_officer] is an instance of [leader] –[leader] is an instance of [person,individual,someone,somebody,mortal,human,soul] –[man] is an instance of [person,individual,someone,somebody,mortal,human,soul] Man ~ paper_bag –man/n is in [man,piece] –[man,piece] is an instance of [game_equipment] –[game_equipment] is an instance of [equipment] –[equipment] is an instance of [instrumentality,instrumentation] –[container] is an instance of [instrumentality,instrumentation] –[bag] is an instance of [container] –[sack,poke,paper_bag,carrier_bag] is an instance of [bag] –paper_bag/n is in the synset [sack,poke,paper_bag,carrier_bag]

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Example 3 (Closest Match) –WordNet Vote ~ election –vote/n is in [vote] –[election] is an instance of [vote] Vote ~ weight –vote/v is in [vote] –[vote] is an instance of [choose,take,select,pick_out] –[choose,take,select,pick_out] is an instance of [decide,make_up_one's_mind,determine] –determine and determine/v related by verb.cognition –[predetermine] is an instance of [determine,shape,mold,influence,regulate] –predetermine and predetermine/v related by verb.cognition –[slant,angle,weight] is an instance of [bias,predetermine] –weight/v is in the synset [slant,angle,weight]

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Machine Translation by Analogy –Fundamental ideas Man does not translate a simple sentence by doing deep linguistic analysis Instead, decompose input into case frame, translate each item of the frame, and compose the result into one sentence Transfer is done by the analogy translation principle using a database of examples –European languages Translation will be possible without great structural changes –English/Japanese a different matter Advantage of EBMT is that it bypasses detailed syntactic analysis and correspondence (BTW, what natural language would be a good interlingua for English/Japanese? )

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Machine Translation by Analogy –Example To my regret, I cannot go tomorrow I am sorry I cannot visit tomorrow It is a pity that I cannot go tomorrow Sorry, tomorrow I will not be available regretthoughtomorrow-topicgonot disappointmentinspite ofvisit whileattend with

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Machine Translation by Analogy –Example A book in which the affairs of international politics is written A book in which (someone) wrote about the events of international politics A book written about the events of international politics A book on international politics international politics ofmatteraboutwritebook thingofdrawvolume affaironwork situationwith event

Paper 31. A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. Machine Translation by Analogy –Need Database of example sentences Mechanism of finding analogical example sentences –Learning/Acquisition Easy to add new words and new examples –Data is solid and does not change Does not require linguistic theory –Theories come and go Does not require deep syntactic analysis –Pre-processing stage Input can be simplified in some cases to get a better match