CS460/626 : Natural Language Processing/Speech, NLP and the Web

(Lecture 1 – Introduction)

Pushpak BhattacharyyaCSE Dept., IIT Bombay4th Jan, 2011

Persons involved Faculty instructors: Dr. Pushpak

Bhattacharyya (www.cse.iitb.ac.in/~pb)

TAs: Joydip Datta, Debarghya Majumdar {joydip,deb}@cse

Course home page (to be created) www.cse.iitb.ac.in/~cs626-460-2011

Perpectivising NLP: Areas of AI and their inter-dependencies

Search

Vision

PlanningMachine Learning

Knowledge RepresentationLogic

Expert SystemsRoboticsNLP

Books etc. Main Text(s):

Natural Language Understanding: James Allan Speech and NLP: Jurafsky and Martin Foundations of Statistical NLP: Manning and Schutze

Other References: NLP a Paninian Perspective: Bharati, Cahitanya and Sangal Statistical NLP: Charniak

Journals Computational Linguistics, Natural Language Engineering, AI, AI

Magazine, IEEE SMC Conferences

ACL, EACL, COLING, MT Summit, EMNLP, IJCNLP, HLT, ICON, SIGIR, WWW, ICML, ECML

Allied DisciplinesPhilosophy Semantics, Meaning of “meaning”, Logic

(syllogism)Linguistics Study of Syntax, Lexicon, Lexical Semantics etc.

Probability and Statistics Corpus Linguistics, Testing of Hypotheses, System Evaluation

Cognitive Science Computational Models of Language Processing, Language Acquisition

Psychology Behavioristic insights into Language Processing, Psychological Models

Brain Science Language Processing Areas in Brain

Physics Information Theory, Entropy, Random Fields

Computer Sc. & Engg. Systems for NLP

Topics proposed to be covered

Shallow Processing Part of Speech Tagging and Chunking using HMM, MEMM, CRF, and

Rule Based Systems EM Algorithm

Language Modeling N-grams Probabilistic CFGs

Basic Speech Processing Phonology and Phonetics Statistical Approach Automatic Speech Recognition and Speech Synthesis

Deep Parsing Classical Approaches: Top-Down, Bottom-UP and Hybrid Methods Chart Parsing, Earley Parsing Statistical Approach: Probabilistic Parsing, Tree Bank Corpora

Topics proposed to be covered (contd.) Knowledge Representation and NLP

Predicate Calculus, Semantic Net, Frames, Conceptual Dependency, Universal Networking Language (UNL)

Lexical Semantics Lexicons, Lexical Networks and Ontology Word Sense Disambiguation

Applications Machine Translation IR Summarization Question Answering

Grading Based on

Midsem Endsem Assignments Paper-reading/SeminarExcept the first two everything else in

groups of 4. Weightages will be revealed soon.

Definitions etc.

What is NLP Branch of AI 2 Goals

Science Goal: Understand the way language operates

Engineering Goal: Build systems that analyse and generate language; reduce the man machine gap

The famous Turing Test: Language Based Interaction

Machine

Human

Test conductor

Can the test conductor find out which is the machine and which the human

Inspired Eliza http://www.manifestation.com/

neurotoys/eliza.php3

Inspired Eliza (another sample interaction)

A Sample of Interaction:

“What is it” question: NLP is concerned with Grounding

Ground the language into perceptual, motor and cognitive capacities.

GroundingChair

Computer

Two Views of NLP and the Associated Challenges

1. Classical View2. Statistical/Machine

Learning View

Stages of processing Phonetics and phonology Morphology Lexical Analysis Syntactic Analysis Semantic Analysis Pragmatics Discourse

Phonetics Processing of speech Challenges

Homophones: bank (finance) vs. bank (river bank) Near Homophones: maatraa vs. maatra (hin) Word Boundary

aajaayenge (aa jaayenge (will come) or aaj aayenge (will come today)

I got [ua]plate Phrase boundary

mtech1 students are especially exhorted to attend as such seminars are integral to one's post-graduate education

Disfluency: ah, um, ahem etc.

Morphology Word formation rules from root words Nouns: Plural (boy-boys); Gender marking (czar-czarina) Verbs: Tense (stretch-stretched); Aspect (e.g. perfective

sit-had sat); Modality (e.g. request khaanaa khaaiie) First crucial first step in NLP Languages rich in morphology: e.g., Dravidian,

Hungarian, Turkish Languages poor in morphology: Chinese, English Languages with rich morphology have the advantage of

easier processing at higher stages of processing A task of interest to computer science: Finite State

Machines for Word Morphology

Lexical Analysis Essentially refers to dictionary access

and obtaining the properties of the worde.g. dog

noun (lexical property)take-’s’-in-plural (morph

property)animate (semantic property)4-legged (-do-)carnivore (-do)

Challenge: Lexical or word sense disambiguation

Lexical DisambiguationFirst step: part of Speech Disambiguation

Dog as a noun (animal) Dog as a verb (to pursue)

Sense Disambiguation Dog (as animal) Dog (as a very detestable person)

Needs word relationships in a context The chair emphasised the need for adult

educationVery common in day to day communicationsSatellite Channel Ad: Watch what you want, when

you want (two senses of watch)e.g., Ground breaking ceremony/research

Technological developments bring in new terms, additional meanings/nuances for existing terms

Justify as in justify the right margin (word processing context)

Xeroxed: a new verb Digital Trace: a new expression Communifaking: pretending to talk on

mobile when you are actually not Discomgooglation: anxiety/discomfort

at not being able to access internet Helicopter Parenting: over parenting

Syntax Processing StageStructure Detection

S

NPVP

V NP

Ilike mangoes

Parsing Strategy Driven by grammar

S-> NP VP NP-> N | PRON VP-> V NP | V PP N-> Mangoes PRON-> I V-> like

Challenges in Syntactic Processing: Structural Ambiguity Scope

1.The old men and women were taken to safe locations

(old men and women) vs. ((old men) and women)2. No smoking areas will allow Hookas inside

Preposition Phrase Attachment I saw the boy with a telescope (who has the telescope?) I saw the mountain with a telescope (world knowledge: mountain cannot be an

instrument of seeing) I saw the boy with the pony-tail (world knowledge: pony-tail cannot be an

instrument of seeing)Very ubiquitous: newspaper headline “20 years

later, BMC pays father 20 lakhs for causing son’s death”

Structural Ambiguity… Overheard

I did not know my PDA had a phone for 3 months An actual sentence in the newspaper

The camera man shot the man with the gun when he was near Tendulkar

(P.G. Wodehouse, Ring in Jeeves) Jill had rubbed ointment on Mike the Irish Terrier, taken a look at the goldfish belonging to the cook, which had caused anxiety in the kitchen by refusing its ant’s eggs…

(Times of India, 26/2/08) Aid for kins of cops killed in terrorist attacks

Headache for Parsing: Garden Path sentences Garden Pathing

The horse raced past the garden fell. The old man the boat. Twin Bomb Strike in Baghdad kill 25

(Times of India 05/09/07)

Semantic Analysis Representation in terms of

Predicate calculus/Semantic Nets/Frames/Conceptual Dependencies and Scripts

John gave a book to Mary Give action: Agent: John, Object: Book,

Recipient: Mary Challenge: ambiguity in semantic role labeling

(Eng) Visiting aunts can be a nuisance (Hin) aapko mujhe mithaai khilaanii padegii

(ambiguous in Marathi and Bengali too; not in Dravidian languages)

Pragmatics Very hard problem Model user intention

Tourist (in a hurry, checking out of the hotel, motioning to the service boy): Boy, go upstairs and see if my sandals are under the divan. Do not be late. I just have 15 minutes to catch the train.

Boy (running upstairs and coming back panting): yes sir, they are there.

World knowledge WHY INDIA NEEDS A SECOND OCTOBER (ToI,

2/10/07)

DiscourseProcessing of sequence of sentences Mother to John:

John go to school. It is open today. Should you bunk? Father will be very angry.

Ambiguity of openbunk what?Why will the father be angry?

Complex chain of reasoning and application of world knowledge Ambiguity of father

father as parent or

father as headmaster

Complexity of Connected Text

John was returning from school dejected – today was the math test

He couldn’t control the class

Teacher shouldn’t have made him responsible

After all he is just a janitor

A look at Textual Humour1. Teacher (angrily): did you miss the class

yesterday?Student: not much

2. A man coming back to his parked car sees the sticker "Parking fine". He goes and thanks the policeman for appreciating his parking skill.

3. Son: mother, I broke the neighbour's lamp shade.Mother: then we have to give them a new one.Son: no need, aunty said the lamp shade is irreplaceable.

4. Ram: I got a Jaguar car for my unemployed youngest son.Shyam: That's  a great exchange!

5. Shane Warne should bowl maiden overs, instead of bowling maidens over

Giving a flavour of what is done in NLP: Structure Disambiguation

Scope, Clause and Preposition/Postpositon

Structure Disambiguation is as critical as Sense Disambiguation Scope (portion of text in the scope of a

modifier) Old men and women will be taken to safe

locations No smoking areas allow hookas inside

Clause I told the child that I liked that he came to

the game on time Preposition

I saw the boy with a telescope

Structure Disambiguation is as critical as Sense Disambiguation (contd.) Semantic role

Visiting aunts can be a nuisance Mujhe aapko mithaai khilaani padegii (“I have to give you

sweets” or “You have to give me sweets”) Postposition

unhone teji se bhaaagte hue chor ko pakad liyaa (“he caught the thief that was running fast” or “he ran fast and caught the thief”)

All these ambiguities lead to the construction of multiple parse trees for each sentence and need semantic, pragmatic and discourse cues for disambiguation

Higher level knowledge needed for disambiguation Semantics

I saw the boy with a pony tail (pony tail cannot be an instrument of seeing)

Pragmatics ((old men) and women) as opposed to (old

men and women) in “Old men and women were taken to safe location”, since women- both and young and old- were very likely taken to safe locations

Discourse: No smoking areas allow hookas inside,

except the one in Hotel Grand. No smoking areas allow hookas inside, but

not cigars.

Preposition Attachment Disambiguation

Problem definition 4-tuples of the form V N1 P N2

saw (V) boys (N1) with (P) telescopes (N2)

Attachment choice is between the matrix verb V and the object noun N1

Lexical Association Table (Hindle and Rooth, 1991 and 1993)

From a large corpus of parsed text first find all noun phrase heads then record the verb (if any) that

precedes the head and the preposition (if any) that

follows it as well as some other syntactic

information about the sentence. Extract attachment information

from this table of co-occurrences

Example: lexical association

A table entry is considered a definite instance of the prepositional phrase attaching to the verb if: the verb definitely licenses the prepositional

phrase E.g. from Propbank,

absolve frames absolve.XX: NP-ARG0 NP-ARG2-of obj-ARG1 1 absolve.XX NP-ARG0 NP-ARG2-of obj-ARG1 On Friday , the firms filed a suit *ICH*-1

against West Virginia in New York state court asking for [ARG0 a declaratory judgment] [rel absolving] [ARG1 them] of [ARG2-of liability] .

Core steps

Seven different procedures for deciding whether a table entry is an instance of no attachment, sure noun attach, sure verb attach, or ambiguous attach

able to extract frequency information, counting the number of times a particular verb or noun attaches with a particular preposition

Core steps (contd.)

These frequencies serve as the training data for the statistical model used to predict correct attachment

To disambiguate a sentence, compute the likelihood of the particular preposition given the particular verb and contrast with the likelihood of the preposition given the particular noun i.e., compare P(with|saw) with P(with|telescope)

as in I saw the boy with a telescope

Critique Limited by the number of

relationships in the training corpora Too large a parameter space Model acquired during training is

represented in a huge table of probabilities, precluding any straightforward analysis of its workings

Approach based on Transformation Based Error Driven Learning, Brill and Resnick, COLING 1994

Example Transformations

Initial attach-ments by defaultare to N1 pre-dominantly.

Transformation rules with word classes

Wordnet synsetsandSemantic classes used

Accuracy values of the transformation based approach: 12000 training and 500 test examples

Method Accuracy #of transformation rules

Hindle and Rooth(baseline)

70.4 to 75.8% NA

Transformations 79.2 418

Transformations(word classes)

81.8 266

Maximum Entropy Based Approach: (Ratnaparki, Reyner, Roukos, 1994) Use more features than (V N1)

bigram and (N1 P) bigram Apply Maximum Entropy Principle

Core formulation We denote

the partially parsed verb phrase, i.e., the verb phrase without the attachment decision, as a history h, and

the conditional probability of an attachment as P(d|h),

where d and corresponds to a noun or verb attachment- 0 or 1- respectively.

Maximize the training data log likelihood

--(1)

--(2)

Equating the model expected parameters and training data parameters

--(3)

--(4)

Features Two types of binary-valued

questions: Questions about the presence of

any n-gram of the four head words, e.g., a bigram maybe V == ‘‘is’’, P == ‘‘of’’

Features comprised solely of questions on words are denoted as “word” features

Features (contd.) Questions that involve the class

membership of a head word Binary hierarchy of classes derived

by mutual information

Features (contd.) Given a binary class hierarchy,

we can associate a bit string with every word in the vocabulary

Then, by querying the value of certain bit positions we can construct

binary questions. Features comprised solely of

questions about class bits are denoted as “class” features, and features containing questions about both class bits and words are denoted as “mixed” features.

Word classes (Brown et. al. 1992)

Experimental data size

Performance of ME Model on Test Events

Examples of Features Chosen for Wall St. Journal Data

Average Performance of Human & ME Model on300 Events of WSJ Data

Human and ME model performance on consensus set for WSJ

Average Performance of Human & ME Model on200 Events of Computer Manuals Data

Back-off model based approach (Collins and Brooks, 1995) NP-attach:

(joined ((the board) (as a non executive director)))

VP-attach: ((joined (the board)) (as a non executive

director)) Correspondingly, NP-attach:

1 joined board as director VP-attach:

0 joined board as director Quintuple of (attachment: A: 0/1, V, N1, P, N2) 5 random variables

Probabilistic formulation

Or briefly,

If

Then the attachment is to the noun, else to the verb

Maximum Likelihood estimate

The Back-off estimateo Inspired by speech recognitiono Prediction of the Nth word from previous (N-1) words

Data sparsity problemf(w1, w2, w3,…wn) will frequently be 0 for large values on n

Back-off estimate contd.

The cut off frequencies (c1, c2 ....) are thresholds determining whether to back-off or not at each level- counts lower than ci at stage i are deemed to be too low to give an accurate estimate, so in this case backing-off continues.

Back off for PPT attachment

Note: the back off tuples always retain the preposition

The backoff algorithm

Lower and upper bounds on performance

Lower bound(most frequent) Upper bound

(human expertsLooking at 4 wordonly)

Results

Comparison with other systems

Maxent, Ratnaparkhi et. al.

TransformationLearning,Brill et. al.

Flexible Unsupervised PP Attachment using WSD and Data Sparsity Reduction: (Medimi Srinivas and Pushpak Bhattacharyya, IJCAI 2007)

Unsupervised approach (some way similar to Ratnaparkhi 1998): The training data is extracted from raw text

The unambiguous training data of the form V-P-N and N1-P-N2 TEACH the system how to resolve PP-attachment in ambiguous test data V-N1-P-N2

Refinement of extracted training data. And use of N2 in PP-attachment resolution process.

Flexible Unsupervised PP Attachment using WSD and Data Sparsity Reduction: (Medimi Srinivas and Pushpak Bhattacharyya, IJCAI 2007)

PP-attachment is determined by the semantic property of lexical items in the context of preposition using WordNet

An Iterative Graph based unsupervised approach is used for Word Sense disambiguation (Similar to Mihalcea 2005)

Use of a Data sparseness Reduction (DSR) Process which uses lemmatization, Synset replacement and a form of inferencing. DSRP uses WordNet.

Flexible use of WSD and DSR processes for PP-Attachment

Graph based disambiguation: page rank based algorithm, Mihalcea 2005

Experimental setup Training Data:

Brown corpus (raw text). Corpus size is 6 MB, consists of 51763 sentences, nearly 1 million 27 thousand words.

Most frequent Prepositions in the syntactic context N1-P-N2: of, in, for, to, with, on, at, from, by

Most frequent Prepositions in the syntactic context V-P-N: in, to, by, with, on, for, from, at, of

The Extracted unambiguous N1-P-N2: 54030 and V-P-N: 22362

Test Data: Penn Treebank Wall Street Journal (WSJ) data

extracted by Ratnaparkhi It consists of V-N1-P-N2 tuples: 20801(training),

4039(development) and 3097(Test)

Experimental setup contd. BaseLine:

The unsupervised approach by Ratnaparkhi, 1998 (Base-RP).

Preprocessing: Upper case to lower case Any four digit number less than 2100 as a

year Any other number or % signs are converted

to num Experiments are performed using DSRP: with

different stages of DSRP Experiments are performed using GuWSD

and DSRP: with different senses

The process of extracting training data: Data Sparsity Reduction

Tools/process Output

Raw Text The professional conduct of the doctors is guided by Indian Medical Association.

POS Tagger The_DT professional_JJ conduct_NN of_IN the_DT doctors_NNS is_VBZ guided_VBN by_ IN Indian_NNP Medical_NNP Association_NNP._.

Chunker [The_DT professional_JJ conduct_NN ] of_IN [the_DT doctors_NNS ] (is_VBZ guided_VBN) by_IN [Indian_NNP Medical_NNP Association_NNP].After replacing each chunk by its head word it results in: conduct_NN of_IN doctors_NNS guided_VBN by_IN Association_NNP

Extraction Heuristics

N1PN2: conduct of doctors and VPN: guided by Association

Morphing N1PN2: conduct of doctor and VPN: guide by association

DSRP (Synset Replacement)

N1PN2: {conduct, behavior} of {doctor, physician} can result in 4 combination with the same sense and similarly for VPN: {guide, direct} by {association} can result in 2 combinations with the same sense.

Data Sparsity Reduction: Inferencing

If V1-P-N1 and V2-P-N1 exist as also do

V1-P- N2 and V2-P-N2, then if

V3-P-Ni exist (i=1,2), thenwe can infer the existence of

V3-P-NJ (i ≠ j) with a frequency count of V3-P-Ni that can be added to the corpus.

Example of DSR by inferencing V1-P-N1: play in garden and V2-P-N1:

sit in garden V1-P-N2: play in house and V2-P-N2:

sit in house V3-P-N2: jump in house exists Infer the existence of V3-P-N1: jump in garden

Results

Effect of various processes on FlexPPAttach algorithm

Precision vs. various processes