“I know the project is due today but I’m just getting started and I have questions…”

Automatic Part-of-Speech Tagging

Will Styler - LIGN 6

Today’s Plan

• Why computers can’t POS tag like humans

• Creating a corpus for POS-tagging use

• Part-of-Speech Ambiguity

• How does HMM-based POS tagging work?

• POS Tagging is hard

We’ve talked about parts of speech already

Lexical Categories

• Nouns: bike, car, cat, dog, tofu, dude, bling
• Verbs: go, eat, talk, walk, yeet
• Adjectives: lit, sweet, hot, cool, awesome
• Adverbs: well, fast, slowly, easily
• Pre/postpositions: with, from, on, in
• Determiners: the, a, that, this, those
• Pronouns: she, he, him, her, it, I, you, they
• Conjunctions: and, or, whenever, while
• Numeral: one, twice, third
• Interjection: ouch, tsk, damnit!

… but these are linguistic, human categories

• We understand the functional distinction between an adverb and a preposition

• We can talk within a certain language, but understand when the rules change

• We know the semantics of a given word

  • We know that a pipe is an object, and that it has a function that could be verby

We also gave you ‘tests’ to use

• “Can you make it plural? If so, it’s a noun!”

• “Can you inflect it? If so, it’s a verb!”

• “If you can use a comparative construction, it’s probably an adjective!”

• “Pronouns can substitute for noun phrases”

• “Is this a relationship a squirrel can have with a tree? Then probably Preposition!”

… but a computer can’t use any of these tests

• “Sure, rotates is the plural of ‘rotate’, so it’s a noun”

• “I treed, therefore, tree is a verb”

• “This slide is computerer than the last one”

• “What the heck is a noun phrase, anyways?”

• “Squirrel? Tree? Huh?”

So, we can’t teach computers to do POS tagging in the same way that we teach humans to!

Preparing for POS Tagging

Before we can automate it, we need to do it with humans

• This is always going to be the case

Determining the best tagset

• This is partly language specific

  • What POS categories exist

  • What additional detail would be helpful in prediction

• Partly based on what corpora are available

  • Use this tagset, or annotate 12 million words?

For English…

(Table from Jurafsky and Martin ‘Speech and Language Processing’ 3e)

Annotating a corpus for POS tags

• Teach some annotators the POS-tagging system

• Run a sample POS-tagging system to get suggestions

• Have the annotators hand correct them

On/IN an/DT exceptionally/RB hot/JJ evening/NN early/RB in/IN July/NNP a/DT young/JJ man/NN came/VBD out/RP of/IN the/DT garret/NN in/IN which/WDT he/PRP lodged/VBN and/CC walked/VBD slowly/RB ,/, as/RB though/IN in/IN hesitation/NN ,/, towards/IN a/DT bridge/NN ./.

All example tagging from today comes from the Stanford Parser

There are many tagged corpora already out there

• You don’t need to do this.

• Which is good.

• POS tagging is super boring

Once you have a tagset and a corpus, you can use…

Automatic POS Tagging

POS Ambiguity

How much uncertainty there is about the part of speech of a given word

Some words are certain in terms of POS

• ‘Funniest’

• ‘hesitantly’

• ‘Sharon’

• Around 85% of words are unambiguous in terms of POS

  • … but around 65% of tokens in running text are ambiguous :(

Some words are only a bit ambiguous in POS

• ‘in’

• ‘a’

• ‘between’

• ‘Marshall’

• ‘Demonstrated’

Some words are very ambiguous in POS

• ‘sink’

• ‘that’

• ‘lift’

• ‘will’

Some words have many parts of speech

• earnings growth took a back/JJ seat
• a small building in the back/NN
• a clear majority of senators back/VBP the bill
• Dave began to back/VB toward the door
• enable the country to buy back/RP about debt
• I was twenty-one back/RB then

POS tagging is about resolving this ambiguity

The Stupid Approach: ‘Most Frequent Tag’

• “Let the tag of word X be the most likely tag of word X in our corpus”

• Tagging is just a lookup table

  • ‘fly’ is most frequently a verb

  • Therefore, every instance of ‘fly’ is a verb

• This provides a ‘baseline’ performance

  • “If we take the dumbest possible approach, what performance do we get?”

Most Frequent Tag Accuracy

• Accuracy here is ‘percentage of tags correctly labeled’

• Most Frequent Tag gets 92% accuracy on WSJ data!

• If we want to use something more complicated, you have to do better than this.

  • If you can’t beat the dumbest approach, you’ve got a problem

Slightly more intelligent: Word form features

• Capitalization

  • ‘I showed Will my will, prepared by Green.’

• Prefixes and suffixes are helpful.

  • ‘Ungerplinked’

  • ‘Flabertibly’

  • ‘Skwerking’

• X-Y constructions are usually adjectives

  • “New-found”

  • “46-year”

  • “Under-utilized”

… but words come in sequences. We should use that!

HMM-based POS Tagging

Hidden Markov Model

A machine learning process which models a series of observations, with the assumption that there’s some ‘hidden’ state which helps to predict the observations

One major assumption of HMMs

• The probability of the current state is based ONLY on the previous state

• The model does not have long term ‘memory’

• The model cannot look ahead

• This is a left-to-right walk through the data

HMMs for POS Tagging

• Observations: The series of words in the text

• States: The parts of speech of those words

• ‘Look at the sequence of words, to help predict which part of speech corresponds to this word’

How do we use HMMs for POS-tagging

• 1: Calculate the probabilities of parts-of-speech (and sequences) from a corpus

• 2: Tokenize the input data

• 3: Using the input, decide the most likely sequence of parts-of-speech

We need to know two types of probabilities

• Observation probability: The probability that a word has a given tag

  • e.g. “How likely is”will” to be a modal verb?”

• Transition Probability: The probability of one POS, given the prior one

  • e.g. “How likely is a modal verb following a pronoun?”

To get observation probabilities…

• Count the number of instances of “will” in the corpus

• Count the number of times that it’s a modal verb

• Count the number of times it’s a noun

• Count the number of times it’s a proper noun

• … and so on …

• Turn these numbers into P(modal|will) (and so on)

Observation probability gets at the idea of ‘POS Ambiguity’

• Words that have little ambiguity will have high probabilities for one category

• Words that have lots of ambiguity may have nearly equivalent probabilties across several categories

To get Transition probabilities…

• Count the number of instances of modal in the corpus

• Count the number of times modal follows pronoun

• Count the number of times modal follows noun

• Count the number of times modal follows verb

• … and so on …

• Turn these numbers into P(modal|Previous pronoun) (and so on)

Transition probabilities get at the idea that syntax involves sequences of word types

• How likely is a Determiner to be followed by a Noun?

  • REALLY likely

• How likely is a preposition to be followed by a determiner?

  • Reasonably likely

• How likely is a preposition to be followed by a proper noun?

  • Likely-ish

• How likely is a modal verb (e.g. ‘will’) to be followed by a Noun?

  • Really unlikely

Now we know the probabilities!

• Then we tokenize

• Then…

We decode the HMM

• “Given this sequence of words, what’s the most likely sequence of POS tags”

• This uses the Viterbi Algorithm

  • Which we’re not going into!

HMM Decoding: The Basic Idea

• We know the probability of a given state (POS tag) given each word

• We know the probability of a given state (POS tag) given the prior state (POS tag)

• We can calculate the most probable state for each word in light of those two facts

• What is the most likely string of states that gets us through the entire sentence?

So, we have the most likely set of POS tags

• Both with respect to individual words’ probabilities

• … and with respect to the likely sequence of tags

• This gives us the best of both worlds!

• Cool!

One consequence of HMM-based tagging

• Word order matters!

the/DT three/CD cute/JJ cats/NNS made/VBN will/MD sit/VB back/RP in/IN awe/NN

• sit/VB cute/JJ three/CD awe/NN the/DT will/NN back/RB made/VBN in/IN cats/NNS

• ‘will’ goes from modal to noun

• ‘back’ goes from particle to adverb

How does HMM-based POS tagging perform?

• Baseline (“Most Frequent Class”): ~92% accuracy

• Hidden Markov Model POS Tagging: ~97% accuracy

• That’s pretty good!

• This is one of the ‘flagship’ tasks for HMMs

  • Other approaches exist

  • Neural Networks didn’t win, for once!

  • (Well, OK, they might win by a few decimal places)

… Why only 97% accuracy?

POS Tagging is hard

Use-mention distinctions

Not all words are being used, when being used

• You can have words that show up in uninformative contexts

• Words that are being mentioned, rather than used, are hard to POS-tag

‘She said ’bear’ was her favorite word.’

• She/PRP said/VBD `/`` bear/NN ‘/’’ was/VBD her/PRP$ favorite/JJ word/NN ./.

‘Roger texted me ’back’’

• Roger/NNP texted/VBD me/PRP `/`` back/VBP ‘/’’

‘I bought the The Pianist DVD’

• I/PRP bought/VBD the/DT The/NNP Pianist/NNP DVD/NN

• I/PRP bought/VBD the/DT the/DT pianist/NN DVD/NN

Ambiguous Sentences

Some sentences are actually ambiguous in POS tagging

• Not all ambiguities of POS are resolveable by humans

‘Maria was entertaining last night’

• Maria/NNP was/VBD entertaining/JJ last/JJ night/NN

‘I saw the official take from the store.’

• I/PRP saw/VBD the/DT official/NN take/VBP from/IN the/DT store/NN ./.

‘You should ask a Smith.’

• You/PRP should/MD ask/VB a/DT Smith/NNP ./.

‘I hate bridging gaps.’

• I/PRP hate/VBP bridging/VBG gaps/NNS ./.

Rare or Unknown words

Rare or unknown Words

• Capitalization and Morphology are the best tools

• You can rely mostly on the transitional probability within the model

  • “Well, I know that the last thing was a modal ‘will’, so ‘gerfleeble’ is probably a verb!”

‘yeet’

• yeet/NN

‘yeeting’

• yeeting/NN

‘yeeted’

• yeeted/JJ

‘I yeet when I throw empty cans’

• I/PRP yeet/VBP when/WRB I/PRP throw/VBP empty/JJ cans/NNS

‘lit’

• lit/UH

‘That phonetics lab meeting was lit’

• That/DT phonetics/NNS lab/NN meeting/NN was/VBD lit/JJ

‘I’m studying English Lit’

• I/PRP ’m/VBP studying/VBG English/NNP Lit/NNP

‘They lit the beacon of Amon Din to summon the Rohirrim’

• They/PRP lit/VBD the/DT beacon/NN of/IN Amon/NNP Din/NNP to/TO summon/VB the/DT Rohirrim/NNP

Homonyms

Homonyms are (always) a problem

• Is ‘saw’ a past tense verb, a noun, or a present tense verb?

‘I saw the sign’

• I/PRP saw/VBD the/DT sign/NN

‘I saw the sign whenever I need to test the cutting feel of a new blade’

• I/PRP saw/VBD the/DT sign/NN whenever/WRB I/PRP need/VBP to/TO test/VB the/DT cutting/VBG feel/NN of/IN a/DT new/JJ blade/NN

‘I bought a saw’

• I/PRP bought/VBD a/DT saw/NN

POS Tagging is crucial

POS Tagging is very helpful

• Helps disambiguate word senses

• Helps identify verbs vs. the things the verbs are acting on

• Provides the basis for syntactic parsing!

Wrapping up

• Computers can’t use meaning or language intuitions to POS-tag

• POS-tagged data is valuable

• Words can be more or less ambiguous in terms of POS tags

• HMMs work great for POS Tagging

• But POS tagging is still hard!

For Next Time

• We’ll talk about syntactic parsing

Thank you!