1 SIMS 290-2: Applied Natural Language Processing Marti Hearst Sept 22, 2004

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SIMS 290-2: Applied Natural Language Processing

Marti HearstSept 22, 2004

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Today

Cascaded ChunkingExample of Using Chunking: Word AssociationsEvaluating ChunkingGoing to the next level: Parsing

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Cascaded Chunking

Goal: create chunks that include other chunksExamples:

PP consists of preposition + NPVP consists of verb followed by PPs or NPs

How to make it work in NLTKThe tutorial is a bit confusing, I attempt to clarify

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Creating Cascaded Chunkers

Start with a sentence tokenA list of words with parts of speech assignedCreate a fresh one or use one from a corpus

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Creating Cascaded Chunkers

Create a set of chunk parsersOne for each chunk typeEach one takes as input some kind of list of tokens, and produced as output a NEW list of tokens

– You can decide what this new list is called Examples: NP-CHUNK, PP-CHUNK, VP-CHUNK

– You can also decide what to name each occurrence of the chunk type, as it is assigned to a subset of tokens

Examples: NP, VP, PP

How to match higher-level tags?It just seems to match their string descriptionSo best be certain that their name does not overlap with POS tags too

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Let’s do some text analysis

Let’s try this on more complex sentencesFirst, read in part of a corpusThen, count how often each word occurs with each POSDetermine some common verbs, choose oneMake a list of sentences containing that verbTest out the chunker on them; examine further

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Why didn’t this parse work?

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Why d

idn’t

this

pars

e w

ork

?

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Corpus Analysis for Discovery ofWord Associations

Classic paper by Church & Hanks showed how to use a corpus and a shallow parser to find interesting dependencies between words

– Word Association Norms, Mutual Information, and Lexicography, Computational Linguistics, 16(1), 1991

– http://www.research.att.com/~kwc/publications.html

Some cognitive evidence:Word association norms: which word to people say most often after hearing another word

– Given doctor: nurse, sick, health, medicine, hospital…

People respond more quickly to a word if they’ve seen an associated word

– E.g., if you show “bread” they’re faster at recognizing “butter” than “nurse” (vs a nonsense string)

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Idea: use a corpus to estimate word associationsAssociation ratio: log ( P(x,y) / P(x)P(y) )

The probability of seeing x followed by y vs. the probably of seeing x anywhere times the probability of seeing y anywhereP(x) is how often x appears in the corpusP(x,y) is how often y follows x within w wordsInteresting associations with “doctor”:

– X: honorary Y: doctor– X: doctors Y: dentists– X: doctors Y: nurses– X: doctors Y: treating– X: examined Y:doctor– X: doctors Y: treat

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Now let’s make use of syntactic information.Look at which words and syntactic forms follow a given verb, to see what kinds of arguments it takesCompute triples of subject-verb-object

Example: nouns that appear as the object of the verb usage of “drink”:

– martinis, cup_water, champagne, beverage, cup_coffee, cognac, beer, cup, coffee, toast, alcohol…

– What can we note about many of these words?

Example: verbs that have “telephone” in their object: – sit_by, disconnect, answer, hang_up, tap, pick_up, return,

be_by, spot, repeat, place, receive, install, be_on

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The approach has become standardEntire collections available

Dekang Lin’s Dependency Database– Given a word, retrieve words that had dependency

relationship with the input word

Dependency-based Word Similarity– Given a word, retrieve the words that are most similar

to it, based on dependencies

http://www.cs.ualberta.ca/~lindek/demos.htm

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Example Dependency Database: “sell”

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Example Dependency-based Similarity: “sell”

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Homework Assignment

Choose a verb of interestAnalyze the context in which the verb appears

Can use any corpus you like– Can train a tagger and run it on some fresh text

Example: What kinds of arguments does it take?Improve on my chunking rules to get better characterizations

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Evaluating the Chunker

Why not just use accuracy?Accuracy = #correct/total number

DefinitionsTotal: number of chunks in gold standardGuessed: set of chunks that were labeledCorrect: of the guessed, which were correctMissed: how many correct chunks not guessed?Precision: #correct / #guessedRecall: #correct / #totalF-measure: 2 * (Prec*Recall) / (Prec + Recall)

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Example

Assume the following numbersTotal: 100Guessed: 120Correct: 80Missed: 20Precision: 80 / 120 = 0.67Recall: 80 / 100 = 0.80F-measure: 2 * (.67*.80) / (.67 + .80) = 0.69

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Evaluating in NLTKWe have some already chunked text from the Treebank

The code below uses the existing parse to compare against, and to generate Tokens of type word/tag to parse with our own chunker.

Have to add location information so the evaluation code can compare which words have been assigned which labels

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How to get better accuracy?

Use a full syntactic parserThese days the probabilistic ones work surprisingly well

They are getting faster too.Prof. Dan Klein’s is very good and easy to run

– http://nlp.stanford.edu/downloads/lex-parser.shtml

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Next Week

Shallow Parsing AssignmentDue on Wed Sept 29

Next week:Read paper on end-of-sentence disambiguationPresley and Barbara lecturing on categorizationWe will read the categorization tutorial the following week

Documents

1 SIMS 290-2: Applied Natural Language Processing Marti Hearst Sept 22, 2004