cs 8520: artificial intelligence

CSC 8520 Spring 2013. Paula Matuszek

CS 8520: Artificial Intelligence

Natural Language Processing Introduction

Paula Matuszek

Spring, 2013

2CSC 8520 Spring 2013. Paula Matuszek

Natural Language Processing• speech recognition

• natural language understanding

• computational linguistics

• psycholinguistics

• information extraction

• information retrieval

• inference

• natural language generation

• speech synthesis

• language evolution


Applied NLP• Machine translation

• spelling/grammar correction

• Information Retrieval

• Data mining

• Document classification

• Question answering, conversational agents


Natural Language Understanding

accoustic /phonetic

morphological/syntactic

semantic / pragmatic

sound wavessound waves

internal internal representationrepresentation


Sounds Symbols Sense

Natural Language Understanding

accoustic /phonetic






•“How to recognize speech, not to wreck a nice beach”

•“The cat scares all the birds away”

•“The cat’s cares are few”

Where are the words? sound wavessound waves


accoustic /phonetic



- pauses in speech bear little relation to word breakspauses in speech bear little relation to word breaks+ intonation offers additional clues to meaning+ intonation offers additional clues to meaning


•“The dealer sold the merchant a dog”

• “I saw the Golden bridge flying into San Francisco”

Dissecting words/sentences


accoustic /phonetic





• “I saw Pathfinder on Mars with a telescope”

• “Pathfinder photographed Mars”

• “The Pathfinder photograph from Ford has arrived”

• “When a Pathfinder fords a river it sometimes mars its paint job.”

What does it mean?



accoustic /phonetic




What does it mean?



accoustic /phonetic



• “Jack went to the store. HeHe found the milk in aisle 3. HeHe paid for itit and left.”

• “ Q: Did you read the report?

A: I read Bob’s email.”

10


The steps in NLP• Morphology: Concerns the way words are

built up from smaller meaning bearing units.

• Syntax: concerns how words are put together to form correct sentences and what structural role each word has

• Semantics: concerns what words mean and how these meanings combine in sentences to form sentence meanings

Taken from ocw.kfupm.edu.sa/user062/ICS48201/NL2introduction.ppt

11


The steps in NLP (Cont.)• Pragmatics: concerns how sentences are

used in different situations and how use affects the interpretation of the sentence

• Discourse: concerns how the immediately preceding sentences affect the interpretation of the next sentence


12


Some of the Tools• Regular Expressions and Finite State

Automata

• Part of Speech taggers

• N-Grams

• Grammars

• Parsers

• Semantic Analysis

13


Parsing (Syntactic Analysis)• Assigning a syntactic and logical form to an input

sentence– uses knowledge about word and word meanings (lexicon)

– uses a set of rules defining legal structures (grammar)

• Paula ate the apple.

• (S (NP (NAME Paula))

• (VP (V ate)

• (NP (ART the)

• (N apple))))


14


Word Sense Resolution • Many words have many meanings or senses

• We need to resolve which of the senses of an ambiguous word is invoked in a particular use of the word

• I made her duck. (made her a bird for lunch or made her move her head quickly downwards?)


15


Reference Resolution• Domain Knowledge (Registration transaction)

• Discourse Knowledge

• World Knowledge

• U: I would like to register in an CSC Course.

• S: Which number?

• U: Make it 8520.

• S: Which section?

• U: Which section is in the evening?

• S: section 1.

• U: Then make it that section.


CSC 8520 Spring 2013. Paula Matuszek Taken from ocw.kfupm.edu.sa/user062/ICS48201/NL2introduction.ppt

Morphological Analysis

Syntactic Analysis

Semantic Semantic AnalysisAnalysis

Discourse Analysis

Pragmatic Analysis

Internal representation

lexicon

user

Surface form

Perform action

stems

parse tree

Resolve references

Stages of NLPStages of NLP

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Human Languages• You know ~50,000 words of primary language,

each with several meanings

• six year old knows ~13000 words

• First 16 years we learn 1 word every 90 min of waking time

• Mental grammar generates sentences -virtually every sentence is novel

• 3 year olds already have 90% of grammar

• ~6000 human languages – none of them simple!

Adapted from Martin Nowak 2000 – Evolutionary biology of language – Phil.Trans. Royal Society London

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Human Spoken language• Most complicated mechanical motion of the human

body– Movements must be accurate to within mm

– synchronized within hundredths of a second

• We can understand up to 50 phonemes/sec (normal speech 10-15ph/sec)– but if sound is repeated 20 times /sec we hear

continuous buzz!

• All aspects of language processing are involved and manage to keep apace

Adapted from Martin Nowak 2000 – Evolutionary biology of language – Phil.Trans. Royal Society London

19


Why Language is Hard• NLP is AI-complete

• Abstract concepts are difficult to represent

• LOTS of possible relationships among concepts

• Many ways to represent similar concepts

• Tens of hundreds or thousands of features/dimensions

20


Why Language is Easy• Highly redundant

• Many relatively crude methods provide fairly good results

21


History of NLP• Prehistory (1940s, 1950s)

– automata theory, formal language theory, markov processes (Turing, McCullock&Pitts, Chomsky)

– information theory and probabilistic algorithms (Shannon)

– Turing test – can machines think?

22


History of NLP• Early work:

– symbolic approach• generative syntax - eg Transformations and Discourse Analysis Project

(TDAP- Harris)

• AI – pattern matching, logic-based, special-purpose systems– Eliza -- Rogerian therapist http://www.manifestation.com/neurotoys/eliza.php3

– stochastic• bayesian methods

– early successes -- $$$$ grants!

– by 1966 US government had spent 20 million on machine translation

• Critics:– Bar Hillel – “no way to disambiguation without deep understanding”

– Pierce NSF 1966 report: “no way to justify work in terms of practical output”

http://www.manifestation.com/neurotoys/eliza.php3

23


History of NLP• The middle ages (1970-1990)

– stochastic• speech recognition and synthesis (Bell Labs)

– logic-based• compositional semantics (Montague)

• definite clause grammars (Pereira&Warren)

– ad hoc AI-based NLU systems• SHRDLU robot in blocks world (Winograd)

• knowledge representation systems at Yale (Shank)

– discourse modeling• anaphora

• focus/topic (Groz et al)

• conversational implicature (Grice)

24


History of NLP• NLP Renaissance (1990-2000)

– Lessons from phonology & morphology successes:

– finite-state models are very powerful

– probabilistic models pervasive

– Web creates new opportunities and challenges

– practical applications driving the field again

• 21st Century NLP– The web changes everything:

– much greater use for NLP

– much more data available

25


Document Features• Most NLP is applied to some quantity of

unstructured text.

• For simplicity, we will refer to any such quantity as a document

• What features of a document are of interest?

• Most common is the actual terms in the document.

26


Tokenization• Tokenization is the process of breaking up a string

of letters into words and other meaningful components (numbers, punctuation, etc.

• Typically broken up at white space.

• Very standard NLP tool

• Language-dependent, and sometimes also domain-dependent.– 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione

• Tokens can also be larger divisions: sentences, paragraphs, etc.

http://en.wikipedia.org/wiki/3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione

27


Lexical Analyser• Basic idea is a finite state machine

• Triples of input state, transition token, output state

• Must be very efficient; gets used a LOT

0

1

2

blankA-Z

A-Z

blank, EOF

28


Design Issues for Tokenizer• Punctuation

– treat as whitespace?

– treat as characters?

– treat specially?

• Case– fold?

• Digits– assemble into numbers?

– treat as characters?

– treat as punctuation?

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NLTK Tokenizer• Natural Language ToolKit

• http://text-processing.com/demo/tokenize/

• Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world.

http://text-processing.com/demo/tokenize/

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Document Features• Once we have a corpus of standardized and possibly

tokenized documents, what features of text documents might be interesting?– Word frequencies: Bag of Words (BOW)

– Language

– Document Length -- characters, words, sentences

– Named Entities

– Parts of speech

– Average word length

– Average sentence length

– Domain-specific stuff

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Bag of Words• Specifically not interested in order

• Frequency of each possible word in this document.

• Very sparse vector!

• In order to assign count to correct position, need to know all the words used in the corpus– two-pass

– reverse-index

32


Simplifying BOW• Do we really want every word?

• Stop words– omit common function words.

– e.g. http://www.ranks.nl/resources/stopwords.html

• Stemming or lemmatization– Convert words to standard form

– lemma is the standard word; stem may not be a word at all

• Synonym matching

• TF*IDF– Use the “most meaningful” words

http://www.ranks.nl/resources/stopwords.html

33


Stemming• Inflectional stemming: eliminate morphological variants

– singular/plural, present/past

– In English, rules plus a dictionary• books -> book, children -> child

– few errors, but many omissions

• Root stemming: eliminate inflections and derivations– invention, inventor -> invent

– much more aggressive

• Which (if either) depends on problem

• http://text-processing.com/demo/stem/

http://text-processing.com/demo/stem/

34


Synonym-matching• Map some words to their synonyms

• http://thesaurus.com/browse/computer

• Problematic in English– requires a large dictionary

– many words have multiple meanings

• In specific domains may be important– biological and chemical domains:

http://en.wikipedia.org/wiki/Caffeine

– any specific domain: Nova, Villanova, V’Nova

http://thesaurus.com/browse/computer

http://en.wikipedia.org/wiki/Caffeine

35


TF-IDF• Word frequency is simple, but:

– affected by length of document– not best indicator of what doc is about

• very common words don’t tell us much about differences between documents

• very uncommon words are typos or idiosyncratic

• Term Frequency Inverse Document Frequency– tf-idf(j) = tf(j)*idf(j). idf(j)=log(N/df(j))

36


Language• Relevant if documents are in multiple

languages

• May know from source

• Determining language itself can be considered a form of NLP.

• http://translate.google.com/?hl=en&tab=wT

• http://fr.wikipedia.org/

• http://de.wikipedia.org/

http://translate.google.com/?hl=en&tab=wT

http://fr.wikipedia.org/wiki/Wikip%25C3%25A9dia:Accueil_principal

http://de.wikipedia.org/wiki/Wikipedia:Hauptseite

37


Document Counts• Number of characters, words, sentences

• Average length of words, sentences, paragraphs

• EG: clustering documents to determine how many authors have written them or how many genres are represented

• NLTK + Python makes this easy

38


Named Entities• What persons, places, companies are mentioned in

documents?

• “Proper nouns”

• One of most common information extraction tasks

• Combination of rules and dictionary– Example rules:

• Capitalized word not at beginning of sentence• Two capitalized words in a row• One or more capitalized words followed by Inc

– Dictionaries of common names, places, major corporations. Sometimes called “gazetteer”

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Parts of Speech• Part-of-Speech (POS) taggers identify

nouns, verbs, adjectives, noun phrases, etc

• Brill is the best-known rule-based tagger

• More recent work uses machine learning to create taggers from labeled examples

• http://text-processing.com/demo/tag/

• http://cst.dk/online/pos_tagger/uk/index.html

http://text-processing.com/demo/tag/

http://cst.dk/online/pos_tagger/uk/index.html

http://cst.dk/online/pos_tagger/uk/index.html

40


Domain-Specific• Structure in document

– email: To, From, Subject, Body

– Villanova Campus Currents: News, Academic Notes, Events, Sports, etc

• Tags in document– Medline corpus is tagged with MeSH terms

– Twitter feeds may be tagged with #tags.

• Intranet documents might have date, source, department, author, etc.

41


N-Grams• These language features will take you a

long way

• But it’s not hard to come up with examples where it fails:– Dog bites man. vs. Man bites dog.

• Without adding explicit semantics we can still get substantial additional information by considering sequences of words.

42


Free Association Exercise

• I am going to say some phrases. Write down the next word or two that occur to you.– Microsoft announced a new security ____

– NHL commissioner cancels rest ____

– One Fish, ______

– Colorless green ideas ______

– Conjunction Junction, what’s __________

– Oh, say, can you see, by the dawn’s ______

– After I finished my homework I went _____.

43


Human Word Prediction• Clearly, at least some of us have the ability

to predict future words in an utterance.

• How?– Domain knowledge– Syntactic knowledge– Lexical knowledge

44


Claim• A useful part of the knowledge needed to

allow Word Prediction can be captured using simple statistical techniques

• In particular, we'll rely on the notion of the probability of a sequence (a phrase, a sentence)

45


Applications• Why do we want to predict a word, given

some preceding words?– Rank the likelihood of sequences containing

various alternative hypotheses, e.g. for automated speech recognition, OCRing.

• Theatre owners say popcorn/unicorn sales have doubled...

– Assess the likelihood/goodness of a sentence, e.g. for text generation or machine translation

• Como mucho pescado. --> At the most fished.

46


Real Word Spelling Errors• They are leaving in about fifteen minuets to go to

her house.• The study was conducted mainly be John Black.• The design an construction of the system will take

more than a year.• Hopefully, all with continue smoothly in my

absence.• Can they lave him my messages?• I need to notified the bank of….• He is trying to fine out.

Example from Dorr, http://www.umiacs.umd.edu/~bonnie/courses/cmsc723-04/lecture-notes/Lecture5.ppt

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Language Modeling • Fundamental tool in NLP

• Main idea: – Some words are more likely than others to

follow each other– You can predict fairly accurately that

likelihood.

• In other words, you can build a language model

Adapted from Hearst, http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/lecture4.ppt

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N-Grams• N-Grams are sequences of tokens.• The N stands for how many terms are used

– Unigram: 1 term

– Bigram: 2 terms

– Trigrams: 3 terms

• You can use different kinds of tokens– Character based n-grams

– Word-based n-grams

– POS-based n-grams

• N-Grams give us some idea of the context around the token we are looking at.

Adapted from Hearst, http://www.sims.berkeley.edu/courses/is290-2/f04/lectures/lecture4.ppt

49


N-Gram Models of Language• A language model is a model that lets us compute

the probability, or likelihood, of a sentence S, P(S).

• N-Gram models use the previous N-1 words in a sequence to predict the next word– unigrams, bigrams, trigrams,…

• How do we construct or train these language models?– Count frequencies in very large corpora

– Determine probabilities using Markov models, similar to POS tagging.

50


Counting Words in Corpora• What is a word?

– e.g., are cat and cats the same word?

– September and Sept?

– zero and oh?

– Is _ a word? * ? ‘(‘ ?

– How many words are there in don’t ? Gonna ?

– In Japanese and Chinese text -- how do we identify a word?

• Back to stemming!

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Terminology• Sentence: unit of written language

• Utterance: unit of spoken language

• Word Form: the inflected form that appears in the corpus

• Lemma: an abstract form, shared by word forms having the same stem, part of speech, and word sense

• Types: number of distinct words in a corpus (vocabulary size)

• Tokens: total number of words

52


Simple N-Grams• Assume a language has V word types in its

lexicon, how likely is word x to follow word y?– Simplest model of word probability: 1/ V

– Alternative 1: estimate likelihood of x occurring in new text based on its general frequency of occurrence estimated from a corpus (unigram probability)

• popcorn is more likely to occur than unicorn

– Alternative 2: condition the likelihood of x occurring in the context of previous words (bigrams, trigrams,…)

• mythical unicorn is more likely than mythical popcorn

53


Computing the Probability of a Word Sequence

• Compute the product of component conditional probabilities?– P(the mythical unicorn) = P(the) P(mythical|

the) P(unicorn|the mythical)

• The longer the sequence, the less likely we are to find it in a training corpus

• P(Most biologists and folklore specialists believe that in fact the mythical unicorn horns derived from the narwhal)

• Solution: approximate using n-grams

54


Bigram Model

• Approximate by – P(unicorn|the mythical) by P(unicorn|mythical)

• Markov assumption: the probability of a word depends only on the probability of a limited history

• Generalization: the probability of a word depends only on the probability of the n previous words– Trigrams, 4-grams, …

– The higher n is, the more data needed to train

– The higher n is, the sparser the matrix.• Leads us to backoff models

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• For N-gram models– ≈

– P(wn-1,wn) = P(wn | wn-1) P(wn-1)

– By the Chain Rule we can decompose a joint probability, e.g. P(w1,w2,w3)

P(w1,w2, ...,wn) = P(w1|w2,w3,...,wn) P(w2|w3, ...,wn) … P(wn-1|wn) P(wn)

For bigrams then, the probability of a sequence is just the product of the conditional probabilities of its bigrams

P(the,mythical,unicorn) = P(unicorn|mythical) P(mythical|the) P(the|<start>)

Using N-Grams

http://www.dcs.qmul.ac.uk/~norman/BBNs/Chain_rule.htm

57


Counts from the Berkeley Restaurant Project

Nth term

N-1 term

58


BeRP Bigram Table

Nth term

N-1 term

60


• P(I want to eat British food) = P(I|<start>) P(want|I) P(to|want) P(eat|to) P(British|eat) P(food|British) = .25*.32*.65*.26*.001*.60 = .000080

• vs. I want to eat Chinese food = .00015

• Probabilities seem to capture ``syntactic'' facts, ``world knowledge'' – eat is often followed by an NP– British food is not too popular

So What?

61


Approximating Shakespeare• As we increase the value of N, the accuracy of the n-gram

model increases, since choice of next word becomes increasingly constrained

• Generating sentences with random unigrams...– Every enter now severally so, let

– Hill he late speaks; or! a more to leg less first you enter

• With bigrams...– What means, sir. I confess she? then all sorts, he is trim, captain.

– Why dost stand forth thy canopy, forsooth; he is this palpable hit the King Henry.

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• Trigrams– Sweet prince, Falstaff shall die.

– This shall forbid it should be branded, if renown made it empty.

• Quadrigrams– What! I will go seek the traitor Gloucester.

– Will you not tell me who I am?

63


• There are 884,647 tokens, with 29,066 word form types, in about a one million word Shakespeare corpus

• Shakespeare produced 300,000 bigram types out of 844 million possible bigrams: so, 99.96% of the possible bigrams were never seen (have zero entries in the table)

• Quadrigrams worse: What's coming out looks like Shakespeare because it is Shakespeare

64


N-Gram Training Sensitivity• If we repeated the Shakespeare experiment

but trained our n-grams on a Wall Street Journal corpus, what would we get?

• This has major implications for corpus selection or design

65


Some Useful Empirical Observations• A few events occur with high frequency• Many events occur with low frequency• You can quickly collect statistics on the high

frequency events• You might have to wait an arbitrarily long time to

get valid statistics on low frequency events• Some of the zeroes in the table are really zeros

But others are simply low frequency events you haven't seen yet. We smooth the frequency table by assigning small but non-zero frequencies to these terms.

66


Smoothing is like Robin Hood: Steal from the rich and give to the poor (in probability mass)

From Snow, http://www.stanford.edu/class/linguist236/lec11.ppt

67


Summary• N-gram probabilities can be used to

estimate the likelihood– Of a word occurring in a context (N-1)– Of a sentence occurring at all

• Smoothing techniques deal with problems of unseen words in a corpus

• N-grams are useful in a wide variety of NLP tasks

68


All Our N-Grams Are Belong to Youhttp://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html

• Google uses n-grams for machine translation, spelling correction, other NLP

• In 2006 they released a large collection of n-gram counts through the Linguistic Data Consortium, based on a trillion web pages– a trillion tokens, 300 million bigrams, about a

billion tri-, four-and five-grams. (http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2006T13)

• This quantity of data makes a qualitative change in what we can do with statistical NLP.

http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html

69


Semantics and Semantic Analysis

70


Meaning• So far, we have focused on the structure of language,

not on what things mean

• We have been doing natural language processing, but not natural language understanding.

• So what is natural language understanding?– Answering an essay question on an exam?

– Deciding what to order at a restaurant by reading a menu?

– Realizing you’ve been insulted?

– Appreciating a sonnet?

• As hard as answering "What is artificial intelligence?"

71


Meaning, cont• On the practical side, we want to “understand”

natural language because BOW and n-gram methods will only take us so far in some things:– Machine translation

– Generation

– Question answering

• So we saw how we could use n-grams to choose the more likely translation for a word given other words. But n-grams can’t give us the potential translations in the first place.

72


Semantics• What kinds of things can we not do well with the

tools we have already looked at?– Retrieve information in response to unconstrained

questions: e.g., travel planning

– Accurate translations

– Play the "chooser" side of 20 Questions

– Read a newspaper article and answer questions about it

• These tasks require that we also consider semantics: the meaning of our tokens and their sequences

73


So What IS Meaning?• From NLP viewpoint, meaning is a mapping from

linguistic forms to some kind of representation of knowledge of the world

• It is interpreted within the framework of some sort of action to be taken.

• Often we manipulate symbols all the way through; the “meaning” is put in by the human user. Translations, for instance.

• But not always – voice command systems, for instance, may map from the representation into actions.

74


Example• Question: Is there a restaurant in King of Prussia

serving vegetarian dinners?

• From a restaurant database– Lemon Grass is a sister restaurant to the one in West

Philadelphia serving traditional Thai dishes in addition to a complete vegetarian Thai menu. The service and atmosphere are quite pleasant. A welcome addition to the King of Prussia area.

• What do we need to know to answer this question from this text?

• Can we unambiguously answer it?

75


Example, continued• Is there a restaurant in King of Prussia serving vegetarian dinners?

Yes.

• Lemon Grass is a sister restaurant to the one in West Philadelphia serving traditional Thai dishes in addition to a complete vegetarian Thai menu. The service and atmosphere are quite pleasant. A welcome addition to the King of Prussia area.

• What do we need to know? – Vegetarian Thai menu = vegetarian dinners– Welcome addition to the KoP area = in KoP

• Can we unambiguously answer it?– No. But we can be fairly certain.

• The only parse that makes sense

• Restaurants normally do serve the dinner meal.

76


Knowledge Representation for NLP• So what representation?

• A useful KR needs to give us a way to encode the knowledge relevant to our problem in a way which allows us to use it. Any representation which does this will work.

• How do we decide what we want to represent?– Entities, categories, events, time, aspect

– Predicates, relationships among entities, arguments (constants, variables)

– And…quantifiers, operators (e.g. temporal)

• How much structure do we capture?

77


Semantic Nets• labeled, directed graph

• nodes represent objects, concepts, or situations– labels indicate the name

– nodes can be instances (individual objects) or classes (generic nodes)

• links represent relationships– the relationships contain the structural information

of the knowledge to be represented

– the label indicates the type of the relationship

78


Semantic Net Examples

Ship

Hull Propulsion

Steamboat

HasAHasA

InstanceOf

giveBob

Candy

Mary

Person

agent recipient

object

InstanceInstance

79


Generic/Individual• Generic describes the idea--the “notion”

– static

• Individual or instance describes a real entity– must conform to notion of generic

– dynamic

– “individuate” or “instantiate”

• A lot of NLP using semantic nets involves instantiating generic nets based on a given piece of text.

80


Individuation examplegivePerson

Thing

Personagent recipient

object

giveBob

Candy

Maryagent recipient

object

Generic RepresentationProcess the sentence“Bob gave Mary some candy”.

Instantiation

81


Frames• Represent related knowledge about a subject

• Frame has a title and a set of slots– Title is what the frame “is” – the concept– Slots capture relationships of the concept to other things

• Typically can be organized hierarchically – Most frame systems have an is-a slot– allows the use of inheritance

• Slots can contain all kinds of items– Rules, facts, images, video, questions, hypotheses, other frames– In NLP, typically capture relationships to other frames or entities

• Slots can also have procedural attachments– on creation, modification, removal of the slot value

82


Simple Frame Example

Slot Name Filler

Restaurant Lemon Grass

Cuisine Thai, Vegetarian

Price Expensive

Service Excellent

Atmosphere Good

Location KoP

Web page Ask Google.

83


Usage of Frames• Most operations with frames do one of two

things:

• Fill slots – Process a piece of text to identify an entity for

which we have a frame

– Fill as many slots as possible

• Use contents of slots– Look up answers to questions

– Generate new text

[Rogers 1999]

84


Scripts• Describe typical events or sequences

• Components are– script variables (players, props)– entry conditions– transactions– exit conditions

• Create instance by filling in variables

85


Restaurant Script Example• generic template for restaurants

– different types– default values

• script for a typical sequence of activities at a restaurant

• Often has a frame behind it; script is essentially instantiating the frame

[Rogers 1999]

86


Restaurant ScriptEAT-AT-RESTAURANT Script

Props: (Restaurant, Money, Food, Menu, Tables, Chairs)

Roles: (Hungry-Persons, Wait-Persons, Chef-Persons)

Point-of-View: Hungry-Persons

Time-of-Occurrence: (Times-of-Operation of Restaurant)

Place-of-Occurrence: (Location of Restaurant)

Event-Sequence:

first: Enter-Restaurant Script

then: if (Wait-To-Be-Seated-Sign or Reservations)

then Get-Maitre-d's-Attention Script

then: Please-Be-Seated Script

then: Order-Food-Script

then: Eat-Food-Script unless (Long-Wait) when Exit-Restaurant-Angry Script

then: if (Food-Quality was better than Palatable)

then Compliments-To-The-Chef Script

then: Pay-For-It-Script

finally: Leave-Restaurant Script[Rogers 1999]

87


Comments on Scripts• Obviously takes a lot of time to develop

them initially.– The script itself has much of the knowledge– May be serious overkill for most NLP tasks

• We need this level of detail if we want to include answers based on reasoning like “Most restaurants do serve dinner.”

88


First Order Predicate Calculus (FOPC)

• Terms– Constants: Lemon Grass

– Functions: LocationOf(Lemon Grass)

– Variables: x in LocationOf(x)

• Predicates: Relations that hold among objects– Serves(Lemon Grass,VegetarianFood)

• Logical Connectives: Permit compositionality of meaning– I only have $5 and I don’t have a lot of time

– Have(I,$5) ∧¬ Have(I,LotofTime)

89


Choosing a Representation• We would like our representation to

support:– Verifiability– Unambiguous Representation– Canonical Form– Inference– Expressiveness

90


Verifiability• System can match input representation

against representations in knowledge base. If it finds a match, it can return Yes; Otherwise No.

• Does Lemon Grass serve vegetarian food?Serves(Lemon Grass,vegetarian food)

91


Unambiguous Representation• Single linguistic input can have different meaning

representations• Each representation unambiguously characterizes

one meaning.• Example: small cars and motorcycles are allowed

– car(x) & small(x) & motorcycle(y) & small(y) & allowed(x) & allowed(y)

– car(x) & small(x) & motorcycle(y) & allowed(x) & allowed(y)

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Ambiguity and Vagueness• An expression is ambiguous if, in a given

context, it can be disambiguated to have a specific meaning, from a number of discrete, possible meanings. E.g., bank (financial institution) vs bank (river bank)

• An expression is vague, if it refers to a range of a scalar variable, such that, even in a specific context, it’s hard to specify the range entirely. E.g., he’s tall, it’s warm, etc.

93


Representing Similar Concepts• Distinct inputs could have the same meaning

– Does Lemon Grass have vegetarian dishes?

– Do they have vegetarian food at Lemon Grass?

– Are vegetarian dishes served at Lemon Grass?

– Does Lemon Grass serve vegetarian fare?

• Alternatives– Four different semantic representations

– Store all possible meaning representations in KB

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Canonical Form• Solution: Inputs that mean same thing have

same meaning representation

• Is this easy? No!– Vegetarian dishes, vegetarian food, vegetarian

fare– Have, serve

• What to do?

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How to Produce a Canonical Form• Systematic Meaning Representations can be derived

from thesaurus– food ___

– dish ___|____one overlapping meaning sense

– fare ___|

• We can systematically relate syntactic constructions– [S [NP Lemon Grass] serves [NP vegetarian

dishes]]

– [S [NP vegetarian dishes] are served at [NP Lemon Grass]]

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Inference• Consider a more complex request

– Can vegetarians eat at Lemon Grass?– Vs: Does Lemon Grass serve vegetarian food?

• Why do these result in the same answer?

• Inference: Draw conclusions about truth of propositions not explicitly stored in KB

• serve(Lemon Grass,VegetarianFood) => CanEat(Vegetarians,At Lemon Grass)

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Non-Yes/No Questions• Example: I'd like to find a restaurant where

I can get vegetarian food.

• serve(x,VegetarianFood)

• Matching succeeds only if variable x can be replaced by known object in KB.

98


Meaning Structure of Language• Human Languages

– Display a basic predicate-argument structure– Make use of variables– Make use of quantifiers– Display a partially compositional semantics

99


Compositional Semantics• Assumption: The meaning of the whole is comprised of the

meaning of its parts– George cooks. Dan eats. Dan is sick.

– Cook(George) Eat(Dan) Sick(Dan)

– If George cooks and Dan eats, Dan will get sick.

– (Cook(George) ^ eat(Dan)) --> Sick(Dan)

• Meaning derives from – The people and activities represented (predicates and arguments,

or, nouns and verbs)

– The way they are ordered and related: syntax of the representation, which may also reflect the syntax of the sentence

• Parse inputs and associate meaning as we parse.

100


Feature and Information Extraction

• We don’t always need a complete parse.– We may be after only some specific

information– And we know what that information is

• Lemon Grass is a sister restaurant to the one in West Philadelphia serving traditional Thai dishes in addition to a complete vegetarian Thai menu. The service and atmosphere are quite pleasant. A welcome addition to the King of Prussia area.

– We don’t care at all about the other information

101


Information Extraction• Retrieve some specific information which is located

somewhere in this set of documents.

• Don’t want all the information, just what we have specified.

• Information may occur multiple times in the text but we just need to find it once

• Often what is really wanted from a web search, for instance.

• Often involves long sentences, complex syntax, extended discourse, multiple writers

• May involve sentence fragments and other non-grammatical text

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Some Examples of Information Extraction

• Financial Information– Who is the CEO/CTO of a company?

– What were the dividend payments for stocks I’m interested in for the last five years?

• Biological Information– Are there known inhibitors of enzymes in a pathway?

– Are there chromosomally located point mutations that result in a described phenotype?

• Other typical questions– Who is expert in some area?

– Is there a vegetarian restaurant in KoP?

– Can I get a flight to Chicago tomorrow?

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• Create a model of information to be extracted

• Create knowledge base of rules for extraction– concepts

– relations among concepts

• Process information applying a series of tools

• Always involves some level of domain modeling and considerable natural language processing

Information Extraction -- How

104


Staged Approach• Apply a series of tools to an input text

• At each stage an element of is identified for possible use in the next level

• The end result is a set of relations suitable for entry into a database

105


Cascaded Process• Tokenize

• POS Tag

• Tag named entities: Names, dates, locations

• Tag complex entities and relationships– Grammatical, such as noun phrases– Semantic, such as CEO

• Bind values to tags throughout.

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Named Entities• Named entity recognition…

– Labeling all the occurrences of named entities in a text…

• People, organizations, lakes, bridges, hospitals, mountains, etc…

• This is well-understood technology, readily available and works well.

• Typically uses a combination of enumerated lists (often called gazetteers) and regular expressions.

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Complex Entities and Relationships• Uses Named Entities as components• Pattern-matching rules specific to a given domain• May be multi-pass: One pass creates entities

which are used as part of later passes.– EG: First pass locates CEOs, second pass locates dates

for CEOs being replaced.

• May involve syntactic analysis as well, especially for things like negatives and reference resolution

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Why Does This Work?• The problem is very constrained.

– Only looking for a small set of items that can appear in a small set of roles

• We can ignore stuff we don’t care about.

• BUT: creating the knowledge bases can be very complex and time-consuming.

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Summary• Some NLP tasks require some model of meaning: semantics

• Semantics can be modeled in a variety of different formalisms. The goal of all of them is to map from the sample of text being processed into some representation relevant to a real-world task

• Complete generic semantic parses gives us a very rich representation of text, but typically depend on the assumption that language is compositional.

• Other tasks such as Information Extraction may not require a detailed parse, but may depend on extensive domain knowledge

• How you represent and work with semantics will always depend on the NLP task you are trying to accomplish.

cs 8520: artificial intelligence

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