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CS460/626 : Natural Language Processing/Speech, NLP and the Web
(Lecture 37– Semantics; Universal Networking Language)
Pushpak BhattacharyyaCSE Dept., IIT Bombay
12th April, 2011
Semantics: wikipedia
• Semantics (from Greek sēmantiká, neuter plural of sēmantikós) is the study of meaning.
• It typically focuses on the relation between signifiers, such as words, phrases, signs and symbols, and what they stand for, their denotata.
Computational Semantics: wikipedia• Computational semantics is the study of how to
automate the process of constructing and reasoning with meaning representations of natural language expressions.
• Some traditional topics of interest are: construction of meaning representations, semantic underspecification, anaphora resolution, presupposition projection, and quantifier scope resolution.
• Methods employed usually draw from formal semantics or statistical semantics.
• Computational semantics has points of contact with the areas of lexical semantics (word sense disambiguation and semantic role labeling), discourse semantics, knowledge representation and automated reasoning (in particular, automated theorem proving).
• Since 1999 there has been an ACL special interest group on computational semantics, SIGSEM.
A hurdle: signifier-denotata dichotomy
Divide between a word and what it stands for
“red” is NOT red in colour “red wine”, “red rose”, “he is in the
red” denote very different sense of the word
Translation into another language reveals this difference
A Perpective
Morphology
Lexicon
Syntax
Semantics
Pragmatics
Discourse
Our tryst with semantics:
Universal Networking Language (UNL)
Motivation
Extraction of semantics, i.e., deep meaning is important for many applications. Machine Translation, Meaning-based IR, CLIR
Robust, scalable & efficient methods of knowledge extraction required
Machine Translation and Cross Lingual IR: a need of the hour for crossing language barrier
Interlingua: a vehicle for machine translation
Interlingua(UNL)
English
French
Hindi
Chinese
generation
Analysis
UNL: a United Nations project Started in 1996 10 year program 15 research groups across continents First goal: generators Next goal: analysers (needs solving various
ambiguity problems) Current active language groups
UNL_French (GETA-CLIPS, IMAG) UNL_English+Hindi UNL_Italian (Univ. of Pisa) UNL_Portugese (Univ of Sao Paolo, Brazil) UNL_Russian (Institute of Linguistics, Moscow) UNL_Spanish (UPM, Madrid)
10
World-wide Universal Networking Language (UNL) Project
UNL
English Russian
Japanese
Hindi
Spanish
Language independent meaning representation.
Marathi
Others
11
The UNL MT System: an Overview
NLP@IITB
Foundations and Applications
UNL Foundations Semantic Relations Universal Words Attributes How to write UNL expressions
UNL Applications Machine Translation: Rule based and
Statistical Search Text Entailment Sentiment Analysis
LanguageProcessing & Understanding
Information Extraction: Part of Speech tagging Named Entity Recognition Shallow Parsing Summarization
Machine Learning: Semantic Role labeling Sentiment Analysis Text Entailment (web 2.0 applications)Using graphical models, support vector machines, neural networks
IR: Cross Lingual Search Crawling Indexing Multilingual Relevance Feedback
Machine Translation: Statistical Interlingua Based EnglishIndian languages Indian languagesIndian languages Indowordnet
Resources: http://www.cfilt.iitb.ac.inPublications: http://www.cse.iitb.ac.in/~pb
Linguistics is the eye and computation thebody
UNL represents knowledge: John eats rice with a spoon
Semantic relations
attributes
Universal words
Repositoryof 42SemanticRelations and84 attributelabels
Sentence embeddings
Deepa claimed that she had composed a poem.
[UNL]agt(claim.@entry.@past, Deepa)obj(claim.@entry.@past, :01)agt:01(compose.@past.@entry.@complete,
she)obj:01(compose.@past.@entry.@complete,
poem.@indef)
[\UNL]
17
Constituents of Universal Networking Language
Universal Words (UWs) Relations Attributes Knowledge Base
18
UNL Graph
obj
agt
@ entry @ past
minister(icl>person)
forward(icl>send)
mail(icl>collection)
he(icl>person)
@def
@def
gol
He forwarded the mail to the minister.
19
UNL Expression
agt (forward(icl>send).@ entry @ past, he(icl>person))
obj (forward(icl>send).@ entry @ past, minister(icl>person))
gol (forward(icl>send ).@ entry @ past, mail(icl>collection). @def)
20
What is a Universal Word (UW)? Words of UNL Constitute the UNL vocabulary, the
syntactic-semantic units to form UNL expressions
A UW represents a concept Basic UW (an English word/compound
word/phrase with no restrictions or Constraint List)
Restricted UW (with a Constraint List ) Examples:
“crane(icl>device)” “crane(icl>bird)”
21
The Lexicon
Format of the dictionary entry
e.g., [minister] {} “minister(icl>person)” (N,ANIMT,PHSCL,PRSN);
Head word Universal word Attributes
Morphological - Pl(plural), V_ed(past tense form)
Syntactic - V(verb),VOA(verb of action) Semantic - ANIMT(animate), PLACE, TIME
[headword] {} “Universal word“ (Attribute list);
22
The Lexicon (cntd)
Content words:
[forward] {} “forward(icl>send)” (V,VOA) <E,0,0>;
[mail] {} “mail(icl>message)” (N,PHSCL,INANI) <E,0,0>;
[minister] {} “minister(icl>person)” (N,ANIMT,PHSCL,PRSN) <E,0,0>;
Headword Universal Word Attributes
He forwarded the mail to the minister.
23
The Lexicon (cntd)
function words:
[he] {} “he” (PRON,SUB,SING,3RD) <E,0,0>;
[the] {} “the” (ART,THE) <E,0,0>;
[to] {} “to” (PRE,#TO) <E,0,0>;
Headword Universal Word
Attributes
He forwarded the mail to the minister.
Hindi example: सं�ज्ञा� का� उदा�हरण १/२
सं�र्व�भौ�मशब्दाम�ख्य शब्दा
farmer(icl>creator)farmer
शे�तकार�
किकासं�न N,M,ANIMT,FAUNA,MML,PRSN,Na
N,ANIMT,FAUNA,MML,PRSN
E
M
H
N,M,ANIMT,FAUNA,MML,PRSN
गु�ण
25
The Features of a UW
Every concept existing in any language must correspond to a UW
The constraint list should be as small as necessary to disambiguate the headword
Every UW should be defined in the UNL Knowledge-Base
26
Restricted UWs
Examples He will hold office until the spring of next
year. The spring was broken.
Restricted UWs, which are Headwords with a constraint list, for example:
“spring(icl>season)” “spring(icl>device)”“spring(icl>jump)”“spring(icl>fountain)”
27
How to create UWs?
Pick up a concept the concept of “crane"
as "a device for lifting heavy loads” or
as “a long-legged bird that wade in water in search of food”
Choose an English word for the concept. In the case for “crane", since it is a word of
English, the corresponding word should be ‘crane'
Choose a constraint list for the word. [ ] ‘crane(icl>device)' [ ] ‘crane(icl>bird)'
How to create UNL expressions
English sentences: basic structure
A <verb> B John eats bread agt(eat.@entry,
John) obj(eat.@entry,
bread)
A <verb> John sleeps aoj(sleep.@entry,
John)
A <be> B John is good aoj(good.@entry,
John)
verb
A
R1
R2
B
A
aoj
verb
BA
R1R2
Hindi sentences: basic structure
A B <verb> John roti khaataa hai agt(eat.@entry, John) obj(eat.@entry,
bread)
A <verb> John sotaa hai aoj(sleep.@entry,
John)
A <be> B John acchaa hai aoj(good.@entry,
John)
verb
A
R1
R2
B
A
aoj
verb
BA
R1R2
:02:01
Complex English sentences: Use recursion on the basic structure
A <verb> B John who is a good boy eats
bread which is toasted
agt(eat.@entry, :01) obj(eat.@entry, :02) aoj:01(boy, John.@entry) mod:01(boy, good) obj:01(toast,
bread.@entry.@focus)
boy
John
aoj
toast
Bread
obj
eat
:02
:01
agt obj
good
mod
Red arrows indicate entry nodes