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In Defense of Contextual Vocabulary Acquisition:. How to Do Things with Words in Context William J. Rapaport Department of Computer Science & Engineering, Department of Philosophy, and Center for Cognitive Science State University of New York at Buffalo Buffalo, NY 14260

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In defense of contextual vocabulary acquisition l.jpg

In Defense ofContextual Vocabulary Acquisition:

How to Do Thingswith Words in Context

William J. Rapaport

Department of Computer Science & Engineering,

Department of Philosophy, and Center for Cognitive Science

StateUniversityof New Yorkat Buffalo

Buffalo, NY 14260

Contextual vocabulary acquisition l.jpg
Contextual Vocabulary Acquisition

  • CVA = active, deliberate acquisition of a meaning for a word in a text by reasoning from “context”

  • “context” ≠ textual context

    • surrounding words; “co-text”

  • “context” = wide context =

    • “internalized” co-text …

      • ≈ reader’s interpretive mental model of textual “co-text”

    • … “integrated” via belief revision …

      • infer new beliefs from internalized co-text + prior knowledge

      • remove inconsistent beliefs

    • … with reader’s prior knowledge

      • including language knowledge

      • including previous hypotheses about word’s meaning

    • but not including external sources (dictionary, humans)

    •  “context” for CVA is in reader’s mind, not in the text

Overview l.jpg

  • CVA project:

    • computational theory of how to figure out (compute)a meaning for an unfamiliar word from “wide context”.

    • convert algorithms to a teachable curriculum

  • Current status:

    • Have theory

    • Have computational implementation

  • Know that people do “incidental” CVA

  • Possibly best explanation of how we learn vocabulary

    • given # of words high-school grad knows (~45K),& # of years to learn them (~18) = ~2.5K words/year

    • but only taught ~10% in 12 school years

  • 2 groups of researchers say CVA can’t be done (well)

  • This talk: Why they’re wrong.

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What Does ‘Brachet’ Mean?(From Malory’s Morte D’Arthur [page # in brackets])

1. There came a white hart running into the hall with a white brachet next to him, and thirty couples of black hounds came running after them. [66]

  • As the hart went by the sideboard, the white brachet bit him.[66]

  • The knight arose, took up the brachet and rode away with the brachet.[66]

  • A lady came in and cried aloud to King Arthur, “Sire, the brachet is mine”.[66]

  • There was the white brachet which bayed at him fast.[72]

    18. The hart lay dead; a brachet was biting on his throat, and other hounds came behind.[86]

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Computational CVA

  • Based on Karen Ehrlich’s CS Ph.D. dissertation (1995)

  • Implemented in SNePS KRRA system

  • KB: SNePS representation of reader’s prior knowledge

  • I/P: SNePS representation of word & co-text

  • Processing:

    • Inferences drawn/belief revision during text input

      • Simulates reading

    • N & V definition algorithms deductively search this “belief-revised, integrated” KB (the context) for definitional information

  • O/P: Definition frame

    • slots (features): classes, structure, actions, properties, etc.

    • fillers (values): info gleaned from context (= integrated KB)

Slide6 l.jpg

A hart runs into King Arthur’s hall.A white brachet is next to the hart.The brachet bites the hart’s buttock.The knight picks up the brachet.The knight carries the brachet.The lady says that she wants the brachet.

The brachet bays at Sir Tor. [background knowledge: only hunting dogs bay]

--> (defineNoun "brachet")

Definition of brachet:

Class Inclusions: hound, dog,

Possible Actions: bite buttock, bay, hunt,

Possible Properties: valuable, small, white,

I.e. A brachet is a hound (a kind of dog) that can bite, bay, and hunt,

and that may be valuable, small, and white.

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General Comments

  • System’s behavior  human protocols

  • System’s definition  OED’s definition:

    = A brachet is “a kind of hound which hunts by scent”

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I. Are All Contexts Created Equal?

  • Beck, McKeown, & McCaslin (1983),

    • “Vocabulary Development: Not All Contexts Are Created Equal”

    • Elementary School Journal 83(3): 177-181.

  • “it is not true that every context is an appropriate or effective instructional means for vocabulary development”

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Role of Prior Knowledge

  • Beck et al:

    • co-text “can give clues to the word’s meaning”

  • But “clue” is relative:

    • clues need other info to be seenas clues

  • Implication A1:

    • textual clues need to be supplemented with other information to compute a meaning.

  • Supplemental info = reader’s prior knowledge

    • has to be accessible to reader

    • will be idiosyncratic

      Co-text doesn’t suffice; prior knowledge needed

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Do Words Have Unique, Correct Meanings?

  • Beck et al. (& others) assume:

    • A2: A word has a unique meaning

    • A3: A word has a correct meaning

  • Contra “unique”: A word’s meaning varies with:

    • co-text

    • reader(’s prior knowledge)

    • time of reading

  • “Correct” is a red herring (in any case, it’s fishy):

    • Possibly, words have author-intended meanings

      • but these need not be determined by context (textual or wide)

    • Misunderstandings are universally unavoidable

      * Perfect understanding/dictionary definition not needed

      • understanding for passage comprehension suffices

      • reader can always revise definition hypothesis

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Beck et al.’sCategories of Textual Contexts

  • What kinds of co-texts are helpful?

  • But keep in mind that we have different goals:

    • Beck et al.:

      • use co-text to teach “correct” word meanings

    • CCVA:

      • use context to compute word meaning for understanding

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Beck et al.’s Textual Context CategoriesTop-Level Kinds of Co-Text

  • Pedagogical co-texts:

    • artificially constructed, designed for teaching

  • Natural co-texts:

    • “not intended to convey the meaning of a word”

    • 4 kinds (actually, a continuum)

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Beck et al.’s Textual Context Categories1. Misdirective (Natural) Co-Texts

  • “seem to direct student to incorrect meaning for a word”

  • sole example:

    • “Sandra had won the dance contest and the audience’s cheers brought her to the stage for an encore. ‘Every step she takes is so perfect and graceful,’ Ginny said grudgingly, as she watched Sandra dance.”

    • [[grudgingly]] =? admiringly

  • Is this a natural context?

  • Is this all there is to it?..

    • A4: Co-texts have a fixed, usually small size

    • But larger co-text might add information

    • Prior knowledge can widen the context

  • ‘grudgingly’ is an adverb!

    • A5: All words are equally easy to learn

    • But N easier than V, V easier than Adj/Adv! (Granger/Gentner/..Gleitman..)

  • A6: Only 1 co-text can be used.

    • But later co-texts can assist in refining meaning

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Beck et al.’s Textual Context Categories2. Nondirective (Natural) Co-Texts

  • “of no assistance in directing the reader toward any particular meaning for a word”

  • sole example is for an adjective:

    • “Dan heard the door open and wondered who had arrived. He couldn’t make out the voices. Then he recognized the lumbering footsteps on the stairs and knew it was Aunt Grace.”

  • But:

    • Is it natural?

    • What about larger co-text?

    • An adjective!

    • Of no assistance? (see next slide)

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Syntactic Manipulation

  • Misdirective & nondirective contexts can yield correct information!

  • Cf. algebraic manipulation (brings x into focus):

    • 2x + 1 = 7 / x = (7 − 1)/2 = 6/2 = 3

  • Syntactic manipulation (bring hard word into focus):

    • “ ‘Every step she takes is so perfect and graceful,’ Ginny said grudgingly.”

      • ‘Grudgingly’ is the way that Ginny said “…”

      • So, ‘grudgingly’ is a way of saying something

      • In particular, ‘grudgingly’ is a way of (apparently) praising someone’s performance

    • “he recognized the lumbering footsteps on the stairs”

      • ‘lumbering’ is a property of footsteps on stairs

  • Generates initial hypothesis for later refinement

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Beck et al.’s Textual Context Categories3. General (Natural) Co-Texts

  • “provide enough information for reader to place word in a general category”

  • sole example is for an adjective:

    • “Joe and Stan arrived at the party at 7:00. By 9:30 the evening seemed to drag for Stan. But Joe really seemed to be having a good time at the party. ‘I wish I could be as gregarious as he is,’ thought Stan”

  • Same problems, but:

    • adjective is contrasted with Stan’s attitude

    • contrasts are good (so are parallel constructions)

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Beck et al.’s Textual Context Categories4. Directive (Natural) Co-Texts

  • “seem likely to lead the student to a specific, correct meaning for a word”

  • sole example is for a noun:

    • “When the cat pounced on the dog, he leapt up, yelping, and knocked over a shelf of books. The animals ran past Wendy, tripping her. She cried out and fell to the floor. As the noise and confusion mounted, Mother hollered upstairs, ‘What’s all the commotion?’ ”

  • Natural? Long!

  • Noun!

    • note that the sole example of a directive context is a noun, suggesting that it might be the word that makes a context directive

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Beck et al.’s Experiment

  • S’s given passages from “basal” readers (reading textbooks)

  • Researchers categorized co-texts & blacked out words

  • S’s asked to “fill in the blanks with the missing words or reasonable synonyms”

  • Results confirm 4 co-text types

  • Independently of results, there are methodological questions:

    • Are basal readers natural contexts?

    • How large were co-texts?

    • Instruction on how to do CVA?

      • A7: CVA “comes naturally”, so needs no training

    • A8: Fill-in-the-blank tasks are a form of CVA

      • No, they’re not! (see next slide)

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Beck et al.’s ExperimentCVA, Neologisms, & Fill-in-the-Blank

  • Serious methodological problem for all of us:

    • Replacing word with blank or neologismmisleads S’s to find “correct missing/hidden word”

      • ≠ CVA!

  • Our (imperfect) solution:

    • use plausible-sounding neologism

    • tell S it’s like a foreign word with no English equivalent, hence need a descriptive phrase

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Beck et al.’s Conclusion

  • “less skilled readers … receive little benefit from” CVA

  • A9: CVA can only help in learning correct meanings.

  • But:

    • CVA uses same techniques as general reading comprehension:

      • careful, slow reading

      • careful analysis of text

      • directed search for information useful for computing a meaning

      • application of relevant prior knowledge

      • application of reasoning for purpose of extracting information from text

    • CVA, if properly taught & practiced, can improve general reading comprehension

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II. Are Context Clues Unreliable Predictors of Word Meanings?

  • Schatz & Baldwin (1986):

    • “Context Clues Are Unreliable Predictorsof Word Meanings”

    • Reading Research Quarterly 21(4): 439-453.

  • “context does not usually provide clues to the meanings of low-frequency words”

  • “context clues inhibit the correct prediction of word meanings just as often as they facilitate them”

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S&B’s Argument

  • A10: CVA is not an efficient mechanism for inferring word meanings.

  • Because:

    • Co-text can’t help you figure out the correct meaning of an unfamiliar word.

    • (uniqueness & correctness assumptions again!)

  • But, we argue:

    • Wide contextcan help you figure out a meaning for an unfamiliar word.

    • So, context (& CVA) are efficient mechanisms for inferring (better: computing) word meanings.

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Incidental vs. Deliberate CVA

  • S&B:

    • “context clues should help readers to infer meanings of words without the need for readers to interrupt the reading act(*) with diversions to external sources”

  • (*) true for incidental CVA

  • (*) not for deliberate CVA

  • External sources are no solution anyway:

    • Dictionary definitions are just more co-texts! (Schwartz 1988)

    • CVA is base case of recursion, one of whose recursive clauses is: “Look it up in a dictionary”

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S&B’s Experiments

  • 25 natural passages from novels

  • words chosen (the only cited examples):

    • Adj/Adv ~67%

    • N ~27%

    • V ~ 6%

  • But:

    • what are actual %s?

    • which lexical categories were hardest?

    • how do facilitative/confounding co-texts correlate with lexical category?

    • should have had representative sample of 4 co-text categories X 3 or 4 lexical categories

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S&B’s ExperimentsCVA vs. Word-Sense Disambiguation

  • 2 experiments:

    • S’s chose “correct” meaning from list of 5 possible meanings

    • This is WSD, not CVA!

      • WSD = multiple choice

      • CVA = essay question

  • 3rd experiment:

    • real CVA, but “interested only in full denotative meanings or accurate synonyms”

    • cf. assumption A3 about “correct” meanings!

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S&B’s ExperimentsSpace & Time Limits

  • Space limits on size of co-text?

    • S&B: 3 sentences

    • CCVA: start small, work “outward”

  • Time limits on size of co-text?

    • S&B: “all students finished in allotted time”

    • CCVA: no time limits

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S&B’s ExperimentsTeaching CVA

  • S&B: “did not control for S’s knowledge of how to use context clues”

  • CCVA:

    • deliberate CVA is a skill

      • needs to be taught, modeled, & practiced

    • there is other (later) evidence that such training works

      • including “critical thinking” education

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S&B’s 3 Questions(answered in the negative)

  • “Do context clues occur with sufficient frequency to justify them as a major element of reading instruction?”

    • Context clues do occur, & teaching them is justified, if augmented by reader’s prior knowledge & knowledge of CVA skills.

  • “Does context usually provide accurate clues to denotations & connotations of low-frequency words?”

    • CVA can provide clues to revisable hypotheses about unfamiliar word’s meaning

  • Are “difficult words in natural [co-texts] usually amenable to such analysis?”

    • Such words are always amenable to yielding at least some information about their meaning.

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Our CVA Theory

  • Every co-text can give some clue to a word’s meaning.

  • Co-text clues must be supplemented by reader’s prior knowledge.

    • Value of co-text depends on reader’s prior knowledge & ability to integrate them.

  • CVA ≠ fill-in-the-blank; CVA ≠ WSD

  • Co-text size has no arbitrary limits

  • May need lots of co-texts before CVA can asymptotically approach a stable meaning.

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Our CVA Theory (continued)

  • A word does not have a unique meaning

  • A word does not have a correct meaning

    • A word’s “correct” (intended) meaning does not need to be known in order for reader to understand it in context

    • Even familiar/well-known words can acquire new meanings in new contexts.

    • Neologisms usually are learned from context.

  • Some words are easier to compute meanings for than others (N < V < Adj/Adv)

  • CVA is an efficient method for computing word meanings.

  • CVA can improve general reading comprehension

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Our CVA Theory (continued)

  • CVA can (and should) be taught!

    using a curriculum based on our algorithms 