CIS 8590 – Fall 2008 NLP1

Introduction to Natural Language Processing

(aka, Computational Linguistics)

Slides by me, Martha Palmer, Eleni Miltsakaki, Dan Jurafsky,

Tarkan Kacmaz, and others

Overview

• General Methods in NLP (3 weeks)– Low-level NLP problems and techniques– Graphical Models for NLP– Text Mining basics

• Information Retrieval Overview (2 weeks)

• Information Extraction Overview (2 weeks)

• Selected topics in Information Extraction (4 weeks)

CIS 8590 – Fall 2008 NLP2

Practical Matters

• Prereqs: General understanding of probability and statistics

• Grading: • 20% In-class participation, paper presentations• 30% Course projects• 50% Midterm (and possibly quizzes)

• Course projects– Please start finding project partners soon!– I will supply some ideas for projects later

CIS 8590 – Fall 2008 NLP3

WHAT IS LANGUAGE?

When we study human language, we are approaching what some

might call the “human essence”, the distinctive qualities of mind

that are, so far as we know, unique to man.

Noam Chomsky

WHAT IS LANGUAGE?• Definition with respect to form:

Language is a system of speech symbols. It is realized acoustically (sound waves), visually-spatially (sign language) and in written form.

• Definition with respect to function:

Language is the most important means of human communication. It is used to convey and exchange information (informative function)

• Multiplicity of languages:

We know of about 7000 languages, which is about 1% of all the languages that ever existed.

LANGUAGE AND THE BRAIN

LANGUAGE AND THE BRAIN

THEORIES OF LANGUAGE

• Noam Chomsky claims that language is innate.

• B. F. Skinner claims that language is learned; it is basically a stimulus-response mechanism.

WHAT IS GRAMMAR?• When we learn a language we also learn the rules that govern how language elements, such as words, are combined to produce meaningful language.

• These elements and rules constitute the Grammar of a language.

• The Grammar is “what we know”

• Grammar represents our linguistic competence.

DESCRIPTIVE vs PRESCRIPTIVEGRAMMAR

Prescriptive

(should be)

Descriptive

(is)

Areas of Linguistics• phonetics - the study of speech sounds• phonology - the study of sound systems• morphology- the rules of word formation• syntax - the rules of sentence formation• semantics - the study of word meanings• pragmatics – the study of discourse meanings• sociolinguistics - the study of language in society• applied linguistics –the application of the methods and results of linguistics to such areas as language teaching, national language policies, lexicography, translation, language in politics etc.

What is phonetics?

• Phonetics is the science of speech.

• We all speak.

• But how many of us know how we speak?

• Or what speech is like?

• Phonetics seeks to answer those questions.

Orthography and Sounds

• The English language is not phonetic.

• Words are not spelled as they are pronounced

• There is no one to one correspondence between the letters and the sounds or phonemes.

Orthography and Sounds

• Did he believe that Caesar could see the people seize the seas.

• The silly amoeba stole the key to the machine

Articulatory Phonetics

• The production of any speech sound involves the movement of an air stream.

• Most speech sounds are produced by pushing the air out of the lungs through the mouth (oral) and sometimes through the nose (nasal).

SPEECH ORGANSSPEECH ORGANS

Phonology

• Phonology deals with the system and pattern of speech sounds in a language.

• Phonology of a language is the system and pattern of speech sounds.

Phonology

Phonological knowledge permits us to: • produce sounds which form meaningful utterances,

• to recognize a “foreign” accent,

• to make up new words,

• To know what is or is not a sound in one’s language

• to know what different sound strings may represent

Phonetics vs Phonology

Phonetics

The study of speech sounds.

Phonology

The study of the way speech sounds form patterns.

Sequences of Phonemes

b l ı k I b k ı

k l ı b ı l b k

b ı l k b k ı l

k ı l b ı b l k

k k b b l l ı ı

possible impossible

•“I just bought a beautiful new blick” What is a blick?•“I just bought a beautiful new bkli” WHAT!!

Sequences of Phonemes

• Your knowledge of English “tells” you that certain strings of phonemes are permissible and others are not.

• That’s why /bkli/ does not sound like an English word.

• It violates the restrictions on the sequencing of phonemes; i.e. it violates the phonological rules of English.

• Delete a word-final /b/ when it occurs after a /m/as in: But not!

Rules of Phonology

bomb crumb lamb tomb

bombardcrumblelimbertumble

Morphology & Syntax

• Morphology deals with the combination of morphemes into words.

• Syntax deals with the combination of words into sentences.

What is the meaning of ‘meaning’?

• Learning a language includes learning the “agreed upon” meanings of certain strings of sounds and,

• Learning how to combine these meaningful units into larger units which also convey meaning.

Morphemes

• Morpheme is the smallest linguistic unit that has meaning.

• Morpheme is a grammatical unit in which there is an arbitrary union of sound and a meaning and,

• which cannot be further analysed.

Morphemes

• A morpheme may be represented by a single sound:

• e.g. the plural morpheme [s] in cat+s

• A morpheme may be represented by a syllable (monosyllabic):

• e.g. child+ish

Morphemes

A morpheme may be represented by more than one syllable (polysyllabic):

• e.g. lady, water

or three syllables:• e.g. crocodile

or four syllables:• e.g. salamander

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Words

• Two basic ways to form words

– Inflectional (e.g. English verbs)• Open + ed = opened• Open + ing = opening

– Derivational (e.g. adverbs from adjectives, nouns from adjectives)

• Happy happily• Happy happiness (nouns from adjectives)

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Syntax

The study of classes of words

and the rules that govern how the words can combine to make phrases and sentences.

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Basic classes of words

• Classes of words aka parts of speech (POS)– Nouns– Verbs– Adjectives– Adverbs

• The above classes of word belong to the type open class words

• We also have closed class words– Articles, pronouns, prepositions, particles, quantifiers,

conjunctions

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Basic phrases

• A word from an open class can be used to form the basis of a phrase

• The basis of a phrase is called the head

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Examples of phrases

• Noun phrases– The manager of the institute– Her worry to pass the exams– Several students from the English Department

• Adjective phrases– easy to understand– mad as a dog– glad that he passed the exam

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Examples of phrases

• Adverb phrases– fast like the wind– outside the building

• Verb phrases– ate her sandwich– went to the doctor– believed what I told him

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“Complements”

• Notice that to be meaningful the verb “go”, for example requires a phrase for “location”– *John went– John went home

• Such phrases “complete” the meaning of the verb (or other type of head) and are called complements

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Inside the noun phrase

• NPs are used to refer to things: objects, places, concepts, events, qualities, etc

• NPs may consist of:– A single pronoun (he, she, etc)– A name or proper noun (John, Athens, etc)– A specifier and a noun– A qualifier and a noun– A specifier and a qualifier and a noun (e.g., the first

three winners)

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Specifiers

• Specifiers indicate how many objects are described and also how these objects relate to the speaker

• Basis types of specifiers– Ordinals (e.g., first, second)– Cardinals (e.g., one, two)– Determiners (see next slide)

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Determiners

• Basic types of determiners– Articles (the, a, an)– Demonstratives (this, that, these, those)– Possessives (‘s, her, my, whose, etc)– Wh-determiners (which, what –in questions)– Quantifying determiners (some, every, most,

no, any, etc.)

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Qualifiers

• Basic types of qualifiers– Adjectives

• Happy cat• Angry feelings

– Noun modifiers • Cook book• University hospitals

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Inside the verb phrase

• A simple VP– Adverbial modifier + head verb +

complements

• Types of verbs– Auxiliary (be, do, have)– Modal (will, can, could)– Main (eat, work, think)

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Types of verb complements

• Intransitive verbs do not require complements

• Transitive verbs require an object as a complement (e.g. find a key)

• Transitive verbs allow passive forms (e.g. a key was found)

• Ditransitive verbs require one direct and on indirect object (e.g. give Mary a book)

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Other verb complements

• Clausal complements– Some verbs require clausal complements

• Mary knows that John left

• Prepositional phrase complements– Some verbs requires specific PP complements

• Mary gave the book to John

– Others require any PP complement• John put the book on the shelf/in the room/under the table

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Adjective phrases

• Simple– Angry, easy, etc

• Complex – Pleased with the prize– Angry at the committee– Willing to read the book

• Complex AdjP normally do not precede nouns, they are used as complements of verbs such as be or seem

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Adverbial phrases

• Indicators of – Degree– Location– Manner– The time of something (now, yesterday, etc)– Frequency– Duration

• Location in the sentence– Initial – Medial– Final

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Grammars and parsing

• What is syntactic parsing– Determining the syntactic structure of a

sentence

• Basic steps– Identify sentence boundaries– Identify what part of speech is each word– Identify syntactic relations

Context Free Grammar

• S -> NP VP

• NP -> det (adj) N

• NP -> Proper N

• NP -> N

• VP -> V, VP -> V PP

• VP -> V NP

• VP -> V NP PP, PP -> Prep NP

• VP -> V NP NPLING 2000 - 2006 NLP

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LING 2000 - 2006 NLP50

Parses

V PP

VP

S

NP

the

the mat

satcat

onNPPrep

The cat sat on the mat

DetN

Det N

LING 2000 - 2006 NLP51

Parses

VPP

VP

S

NP

time

an arrow

flies

likeNPPrep

Time flies like an arrow.

N

Det N

LING 2000 - 2006 NLP52

Parses

V NP

VP

S

NP

flies like

an

NDet

Time flies like an arrow.

Ntime

arrow

N

LING 2000 - 2006 NLP53

Features

• C for Case, Subjective/Objective– She visited her.

• P for Person agreement, (1st, 2nd, 3rd)– I like him, You like him, He likes him,

• N for Number agreement, Subject/Verb– He likes him, They like him.

• G for Gender agreement, Subject/Verb– English, reflexive pronouns He washed himself.– Romance languages, det/noun

• T for Tense, – auxiliaries, sentential complements, etc. – * will finished is bad

LING 2000 - 2006 NLP54

Probabilistic Context Free Grammars

• Adding probabilities

• Often, lexicalizing the probabilities

A PCFG

• S -> NP VP (0.5)

• S -> ADVP NP VP (0.5)

• NP -> det (adj) N (0.7)

• NP -> Proper N (0.15)

• NP -> N (0.15)

• VP -> V, (0.1); VP -> V PP (0.1)

• VP -> V NP (0.4); VP -> V NP PP (0.4)

• PP -> Prep NP (1)CIS 8590 – Fall 2008 NLP

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A Lexicalized PCFG

Sample rules:

• S_give -> NP VP_give (1.0)

• NP_friend -> det (adj) N_friend (1.0)

• NP_Sally -> ProperN_Sally (1.0)

• VP_give -> V_give NP NP (0.3)

• VP_give -> V_give NP PP_to (0.7)

CIS 8590 – Fall 2008 NLP56

Parsing

Computational task:

Given a set of grammar rules and a sentence, find a valid parse of the sentence (efficiently)

Naively, you could try all possible combinations of rules until you get to a parse tree that has “S” at the root, and the right words at the leaves.

But that takes exponential time in the number of words.

CIS 8590 – Fall 2008 NLP57

CKY Parsing (aka, CYK)

• CKY parsing is a dynamic programming solution

• I bring it up now because dynamic programming shows up all the time in NLP

Dynamic programming: simplifying a complicated problem by breaking it down into simpler subproblems in a recursive manner

CIS 8590 – Fall 2008 NLP58

CKY – Basic Idea

Let the input be a string S consisting of n characters: a1 ... an.

Let the grammar contain r nonterminal symbols R1 ... Rr. This grammar contains the subset Rs which is the set of start symbols.

Let P[n,n,r] be an array of booleans. Initialize all elements of P to false.

At each step, the algorithm sets P[i,j,k] to be true if the subsequence of words (span) starting from i of length j can be generated from Rk

We will start with spans of length 1 (individual words), and then proceed to increasingly larger spans, and determining which ones are valid given the smaller spans that have already been processed.

CIS 8590 – Fall 2008 NLP59

CKY Algorithm

For each i = 1 to n

For each unit production Rj -> ai,

set P[i,1,j] = true.

For each i = 2 to n -- Length of span

For each j = 1 to n-i+1 -- Start of span For each k = 1 to i-1 -- Partition of span

For each production RA -> RB RC

If P[j,k,B] and P[j+k,i-k,C]

then set P[j,i,A] = true

If any of P[1,n,x] is true (x is iterated over the set s, where s are all the indices for Rs)

Then S is member of language

Else S is not member of language CIS 8590 – Fall 2008 NLP

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CKY In Action

http://homepages.uni-tuebingen.de/student/martin.lazarov/demos/cky.html

CIS 8590 – Fall 2008 NLP61

Finding the Best Parse

• With a PCFG (or lexicalized PCFG), it’s possible to score the trees to find the best (highest probability) parse

• Instead of a boolean array P, you would need to store weights (or probabilities) in the array; for the rest, the algorithm is almost identical.

CIS 8590 – Fall 2008 NLP62