Introduction to Computational Linguistics Eleni Miltsakaki AUTH Spring

Introduction to Computational Linguistics Eleni Miltsakaki AUTH Spring 2006 -Lecture 2 1

Outline of English syntax • Words • Phrases • Simple Sentences 2

Review • What is computational linguistics? • What is the subject matter of theoretical computational linguistics? • What is the subject matter of applied computational linguistics? • Why is language hard for the computer? 3

Review • Give examples of – Syntactic ambiguity – Semantic ambiguity – Phonological ambiguity 4

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) 5

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 6

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 7

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 8

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 9

“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 10

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) 11

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) 12

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) 13

Qualifiers • Basic types of qualifiers – Adjectives • Happy cat • Angry feelings – Noun modifiers • Cook book • University hospitals 14

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) 15

Types of verb complements • Intransitive verbs do not required 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) 16

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 17

Adjective phrases • Simple – Angry, easy, etc • Complex – Pleased with the prize – Angry at the committee – Willing to read the book • Complex Adj. P normally do not precede nouns, they are used as complements of verbs such as be or seem 18

Adverbial phrases • Indicators of – – – Degree Location Manner The time of something (now, yesterday, etc) Frequency Duration • Location in the sentence – Initial – Medial – Final 19

The famous argument-adjunct problem • Sometimes it’s hard to say if an adverbial is a verb complement (i. e. it’s an argument of the verb) or simply a modification of the verb phrase (i. e. an adjunct) • Consider – – Mary put the book on the shelf *Mary put Mary painted the room with a brush Mary painted the room 20

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 21

Tree representation • John ate the pizza (S (NP (N John)) (VP (V ate) (NP (Det the) (N cat)))) 22

Some basic tree terminology • • Nodes Links Root Leaves Parent node Child node Ancestor The notion of “domination” 23

How to construct a tree • To construct a tree of an English sentence you need to know which structure are legal in English • Rewrite rules – Describe what tree structures are allowed in the language 24

Rewrite rules for English NP==> N NP==> Det NP VP==> V VP ==> V NP S ==> NP VP ==> N VP ==> John V NP ==> John ate Det N ==> John ate the pizza 25

What makes a good grammar? • Generality – The range of sentences covered by the rules • Selectivity – The range of sentences that can be identified as ungrammatical • Understandability – How simple the grammar is 26

Hint for making rules general • Pay attention to constituents • Diagnostic of constituency – Conjunction • Compare – – – I ate a hamburger and a hot dog I will eat the hamburger and throw away the hot dog I ate a hamburger and John ate a hot dog *I ate a hamburger and on the stove *I ate a cold hot dog and well burned *I ate the hot dog 27

How the conjunction test can help • Compare – I looked up John’s number – I looked up John’s chimney – *I looked up John’s number and in his cupboards – I looked up John’s chimney and in his cupboards 28

Parsing strategies • Top-down – A top down parser starts with S and attempts to rewrite it into a sequence of terminal symbols that matches the words in the input sentence • Bottom-up – You take a sequence of symbols and match it to the right hand side of the rule, i. e. start with Det N and match it to get the NP • Bottom-up chart parsing – To avoid unnecessary repetition of the matching process you use a data structure called chart that allows you to record partial results We’ll see examples in J. Allen’s Natural Language Understanding, Chapter 3 29

What is generative capacity? • The range of languages that a formalism can describe • Formal languages allow a precise (mathematic) characterization • Natural languages CANNOT be characterized precisely enough to define generative capacity 30