Universal Networking Language

Shalini Gupta - 07305R02

The Problem Large exploration of Data Linguistic barriers(Multilingualism)

Web contents are mostly in English and cannot be accessed without some proficiency in this language

Though India forms large part of total population, the proportion of Internet Access is very low.

Need for high speed translation to different languages

Solution: Machine Translation 2 approaches:

Transfer based Works on specific pairs of languages

Some text analysis on source language Some on target language

Interlingua based Build a universal language Convert data to universal language De convert it back Needs only 2N conversions opposed to

N*(N-1) translations for transfer based

UNL: An Interlingua Language independent Knowledge

Representation Vehicle for machine translation UNL solves “Information Monopolies”

problem

Interlingua

(UNL)

Hindi

ChineseFrench

English

Outline Introduction UNL Components Some Controversial Issues in UNL

Language Divergences between Hindi

and English

Conclusion

Introduction to UNL Proposed by the United Nations University Enables computers to process information and

knowledge across the language barriers Replicates functions of natural languages in

human communication Enables distributing, receiving and understanding

multilingual information Represents information sentence by sentence

UNL Graph Each sentence is converted into a hyper graph

Concepts as nodes Relations as directed arcs

Concepts are called Universal Words Word Knowledge represented by Universal

Words (UWs) which are language independent

Conceptual Knowledge captured by relating UWs through relations

Example: John eats rice with a spoon

Universal Word

Attribute

Semantic Relations

UNL Expression

John eats rice with a spoon {unl} agt(eat(icl>do).@entry.@present,

John(iof>person) obj(eat(icl>do).@entry.@present,

rice(icl>food) ins(eat(icl>do).@entry.@present,

spoon(icl>artifact).@indef {/unl}

Universal Word

Types of Universal Word Syntactic and semantic unit of UNL Represents a concept Represents node in graph of UNL expression 2 classes:

Unit concepts Basic UWs Restricted UWs Extra UWs

Compound concepts: Scopes

Types of Universal Words(UWs)

Basic UWs Bare headwords with no constraint list E.g. :

house drink

Restricted UWs Headwords with a constraint list Represents a more specific concept, or subset

of concepts

Types of UWs (contd..)

Constraint List restricts the range of the concept that a Basic UW represents E.g. :

state(icl>country) state(icl>abstract thing)

Extra UWs Special type of Restricted UW Denote concepts that are not present in English. Foreign-language words are used as Head Words E.g. :

Bharatnatyam(icl>dance)

Compound Concepts

Raju said that [he had opened the window]

say (icl>do)

Raju (iof>person

open (icl>do) @entry.@past

@complete

window

(icl>obj) he

@entry.@past

agt obj

agt obj:01

Compound Concepts (contd..)

Set of binary relations that are grouped together to express a compound concept

Interpreted as a whole Expressed by a scope in UNL expressions Raju said that [he had opened the window].

Part of the sentence within square brackets should be grouped

Only when they are grouped together and considered as a whole unit can the correct interpretation be obtained.

Relations

Relation of UNL is expressed as: <relation>(<uw1>, <uw2>) <relation> is one of the relations defined in UNL <uw1>, <uw2> are universal words

E.g. John broke the window agt(break(icl>do).@entry.@past, John(iof>person)) obj(break(icl>do).@entry.@past, window(icl>thing))

41 such relations have been defined

Attributes Describe subjectivity of sentence Enrich the description given by UWs and

relations E.g. Time with respect to the Speaker

happened in the past : @past happening at present : @present will happen in future : @future John broke the window

agt(break(icl>do).@entry.@past, John(iof>person))

UNL Knowledge Base

Defines every possible relation between concepts

Two important roles Defines semantics of Universal Words Gives linguistic knowledge of concepts

E.g. The anchor wrote the script Linguistic Knowledge tells that anchor is a

person Semantics tells that only a person can write a

script (Anchor(of ship) can't do so)

Controversial Issues

Meaning Representation Language: Should provide sufficient means to express

knowledge. Should be simple.

Main expressive device of UNL is

Restrictions

New expressive means for describing UWs have been proposed.

Semantic Restriction

UW: operator(icl>thing) Doesn't effectively separate the meaning 2 meanings

long distance operator(icl>human) addition operator (icl>abstract thing)

Hypernymy and Meronymy are mostly used for expressing restrictions

Synonmy and antonymy can be used E.g. wealth(equ>richness), poor(ant>rich)

Argument Frame Restriction X borrows Y from Z for W All four arguments are needed to define the

action of borrowing completely Example

John borrowed $10000 for 3 years John has been borrowing money for 3 years

UNL as a meaning representation language

should have an ability to draw a distinction between the argument and non-argument links of predicates

Weakly Differentiated Relations

Some relations seem to be weakly differentiated and therefore difficult to use consistently. E.g. gol (final state) – plt (final place) E.g. src (initial state) – plf (initial place)

John went to Brussels can be described both with gol and plt difference is that gol characterizes Brussels

as the final state of John, while plt – as the final place of the whole event

Redundant Relations

Some relations seems to be based more on the semantic class of UWs

E.g. mod (modification) – man (manner) Difference between them boils down to the

semantic class of the starting point of the relation answered politely (man) [to answer] a polite answer (mod) [an answer]

Relations 'man' and 'mod' can be merged

Divergences between English and Hindi

Constituent Order Divergence Jim is playing tennis. जि�म टै�नि�स खे�ल रहा� हा�

(S) (V) (O) (S) (O) (V) Adjunction Divergence

The [living in Delhi] boy दि�ल्ल� म� रहा��वा�ल� लडका�

Preposition-Stranding Divergence Which shop did John go to? निकास दुका�� ��� गया� म�

Divergences(contd..)

Null Subject Divergence �� रहा� हूं� going-am

Pleonastic Divergence It is raining. याहा बा�रिरश हा! र�हा� हा�

Conflational Divergence Jim stabbed him. जि�म उसका! छु$ र� स� म�र�

Promotional Divergence The play is on. खे�ल चल रहा� हा�

Conclusion

UNL is an Interlingua for Machine Translation

Studied Components of UNL

Controversial Issues in UNL

Divergences between English and Hindi

References

Igor Boguslavsky. Some controversial issues of UNL: linguistic aspects. 2004.

Shachi Dave and Pushpak Bhattacharyya. Knowledge extraction from Hindi text, 2001.

Shachi Dave, Jignashu Parikh, and Pushpak Bhattacharyya. Interlingua-based English-Hindi machine translation and language divergence. Machine Translation, 16(4):251–304, 2001.

References The universal networking language manual,

www.undl.org. 2006. Zhu M. Uchida H. The universal networking

language (UNL) specifications. Technical Report, 2005.

Thank You

UNL System

Knowledge Extraction from Hindi Text

EnConverter is a language independent parser

provides framework for analysis Need to provide a lexicon and Analysis Rules Analysis Rule: (<PRE>)... <LNODE>

<RNODE> (<SUF1>) (<SUF2>) (<SUF3>)... <PRI>

Lexicon Entry: [HW] {ID} ”UW” (ATTRIB1, ATTRIB2, ...) <FLG,FRE,PRI>;

Knowledge Extraction from Hindi Text

Each Step: Morphological

Analysis Decision

Relation Lexical

Attribute UNL Attribute

Verbal Concepts Classes of predicates

actions ( have an active initiator, Eg. kill) activities ( set of heterogeneous actions with

common goal, Eg.trade) events (Have no agent, Eg. the bridge

broke ) processes (Denote a situation that occupies

a certain time span, Eg. the tree grows) states (Homogeneous, do not denote a

change, Eg. hear, ache)

Classes of predicates

properties (Differ from the states in that they are atemporal, Eg. blind, red)

relations (Specify relation between two or more things, Eg. love, hate,)

In UNL, all verbal concepts group into three classes (icl>do) contains actions and activities (icl>occur) consists of events and processes (icl>be) composed of states, properties and

relations

Adjectival Concepts

All adjectival concepts are divided into two classes: predicative (aoj>thing) restrictive (mod>thing)

This does not work well in some situations Eg. Wise Greeks diluted wine with water

Restrictive interpretation: ‘Those Greeks who were wise diluted wine with water. Silly ones didn’t’.

Non-restrictive (qualificative) interpretation: ‘Greeks were wise. They diluted wine with water’.

Its restrictive vs qualificative

Should be applied to other modifiers also

The students sitting in the corner are waiting for the professor The students(,) who are sitting in the corner(,) are

waiting for the professor. The students in the corner are waiting for the

professor The phrase 'who are sitting' can be restrictive

(‘those of the students who are sitting in the corner are waiting for the professor; others are not’)

non-restrictive (‘the students are waiting for the professor; they are sitting in the corner’)