Semantic Glimmers: CSDL9

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Slides from a talk I gave in 2009 at Conceptual Structure, Discourse, and Language. Research presented was on contributions of semantic and phonological similarity to sentence comprehension. Also see related paper (Otis & Sagi, 2009).

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Semantic Glimmers: CSDL9

  1. 1. SEMANTIC GLIMMERS: PHONAESTHEMES FACILITATE ACCESS TO SENTENCE MEANING Eyal Sagi & Katya Otis, Northwestern University
  2. 2. <ul><li>Phonaesthemes: phonetic clusters that occur in words that have noticeably similar meanings (Firth, 1930) </li></ul><ul><li>Example: gl- associated with “words relating to light, vision” (glimmer, glisten, glow, glare, glance) </li></ul><ul><ul><li>Violates Saussurean notion of “arbitrariness of the sign” </li></ul></ul><ul><ul><ul><li>Not sound symbolism </li></ul></ul></ul><ul><ul><li>Non-compositional: not properly morphemes </li></ul></ul>Form and Meaning
  3. 3. Form and Meaning: Evidence <ul><li>Subtle regularities in word form can cue syntactic category assignment </li></ul><ul><li>These regularities facilitate sentence comprehension </li></ul><ul><ul><li>Ambiguous sentences can be resolved faster when “nouns sound like nouns; verbs sound like verbs” </li></ul></ul><ul><ul><ul><li>Farmer, Christiansen, & Monaghan, 2006 </li></ul></ul></ul><ul><ul><ul><li>Monaghan, Chater, & Christiansen, 2005 </li></ul></ul></ul>
  4. 4. Phonaesthemes: Previous Research <ul><li>Hutchins (1998) </li></ul><ul><ul><li>Speakers reliably matched phonaestheme-bearing words to glosses of the phonaestheme and vice versa. </li></ul></ul><ul><ul><li>Speakers ranked phonaestheme-bearing words’ coherence with a definition higher when the definition was a gloss of that phonaestheme. </li></ul></ul><ul><ul><li>Found variability in strength of association between 46 phonaesthemes and their glosses. </li></ul></ul><ul><ul><ul><li>Probed speakers’ declarative knowledge about language, not their processing or implicit knowledge </li></ul></ul></ul><ul><ul><ul><li>Words in isolation </li></ul></ul></ul><ul><ul><ul><li>Rely on linguists’ intuitions </li></ul></ul></ul>
  5. 5. Phonaesthemes: Previous Research <ul><li>Bergen (2004) </li></ul><ul><ul><li>Morphological Priming paradigm </li></ul></ul><ul><ul><li>Sharing a phonaestheme provided better priming than either semantic or phonological similarity alone </li></ul></ul><ul><ul><ul><li>Rely on linguists’ intuitions to decide what counts as a phonaestheme </li></ul></ul></ul><ul><ul><ul><li>Words in isolation </li></ul></ul></ul>
  6. 6. Hypotheses <ul><li>Corpus analyses can help reveal regular relationships between form and meaning </li></ul><ul><li>Speakers use these regular relationships in language processing </li></ul><ul><ul><li>Words </li></ul></ul><ul><ul><li>Sentences </li></ul></ul>
  7. 7. Current Studies <ul><li>Corpus analysis of phonetic clusters used by Hutchins (1998) </li></ul><ul><li>Experiment 1: Sentence completion task </li></ul><ul><li>Experiment 2: Paraphrase task </li></ul>
  8. 8. Corpus Analysis: Method <ul><li>Latent Semantic Analysis </li></ul><ul><ul><li>Words as vectors in a semantic space </li></ul></ul><ul><li>Similar meaning  Similar vector direction </li></ul><ul><ul><li>Standard measure: </li></ul></ul><ul><ul><ul><li>Cosine of angle = Correlation between the vectors </li></ul></ul></ul><ul><ul><li>Semantic vectors can be combined </li></ul></ul>gl- (combined vector) ray  glance glisten glare vision light  ’
  9. 9. Corpus Analysis: Method <ul><li>If a cluster of words is semantically related, the respective word vectors will be more correlated than expected by chance. </li></ul><ul><li>Monte-Carlo analysis </li></ul><ul><ul><li>Compare combined vectors of word pairs within a phonaestheme cluster with combined vectors of randomly-chosen word pairs. </li></ul></ul><ul><li>Quantitative criterion for phonaestheme strength </li></ul><ul><ul><li>Measure: # of significant t-tests ( p < .05 ) </li></ul></ul><ul><ul><ul><li>If 15 of 100 t -tests conducted are significant, then the phonaestheme is statistically supported (overall p < .05) </li></ul></ul></ul>
  10. 10. Corpus Analysis: Input <ul><li>Public domain literary works from Project Gutenberg </li></ul><ul><ul><li>4034 documents </li></ul></ul><ul><ul><li>Over 290 million words </li></ul></ul><ul><li>50 candidate phonaesthemes </li></ul><ul><ul><li>46 used by Hutchins (1998) </li></ul></ul><ul><ul><li>4 new clusters: br-, -ign, kn-, z- </li></ul></ul>
  11. 11. Corpus Analysis: Results <ul><li>27 of 50 phonaesthemes met our criterion for significance </li></ul><ul><li>Replicated Hutchins’ survey results </li></ul><ul><ul><li># of significant t-tests correlates with Hutchins’ word-gloss relatedness ratings ( r = 0.53) </li></ul></ul><ul><ul><li># of stems correlates highly with Hutchins’ # of types </li></ul></ul><ul><ul><li>( r = 0.93) </li></ul></ul>
  12. 12. Experiment 1 <ul><ul><li>Hutchins’ gloss-matching experiment tested psychological reality of explicit semantic knowledge about phonaesthemes </li></ul></ul><ul><ul><li>What about implicit, contextual knowledge about phonaesthemes? </li></ul></ul><ul><ul><li>Do phonaesthemes influence our word choice when composing sentences? </li></ul></ul>
  13. 13. Experiment 1: Method <ul><li>36 phonaestheme-bearing from 6 phonaestheme clusters </li></ul><ul><ul><li>kn-, gl-, sn-, -oop, -ump, -ign </li></ul></ul><ul><li>36 accompanying sentence contexts </li></ul><ul><ul><li>Highly congruent with one target word, incongruent with another </li></ul></ul><ul><li>Transformed targets into nonsense words </li></ul><ul><ul><li>Normed for opaqueness: nonsense words whose target was too easily guessed were not used </li></ul></ul>
  14. 14. Experiment 1: Method <ul><ul><li>The stone's _______ flashed from under the leaves. </li></ul></ul><ul><ul><ul><li>lague </li></ul></ul></ul><ul><ul><ul><li>glandor </li></ul></ul></ul><ul><ul><ul><li>thoop </li></ul></ul></ul>
  15. 15. Experiment 1: Results <ul><li>Phonaestheme vector similarity to sentence vector predicted word choice </li></ul><ul><li>( r = .4, p < .05) </li></ul>
  16. 16. Experiment 1: Results <ul><li>Speakers can use phonaesthemes to guess a word’s meaning in sentence contexts </li></ul><ul><li>Phonaestheme’s meaning must cohere with the sentence </li></ul>
  17. 17. Experiment 2: Method <ul><li>Participants asked to read and paraphrase sentences </li></ul><ul><li>Same materials as in experiment 1: </li></ul><ul><ul><li>Congruent: The stone's glandor flashed from under the leaves. </li></ul></ul><ul><ul><li>Incongruent: The stone's thoop flashed from under the leaves. </li></ul></ul><ul><ul><li>Neutral: The stone's lague flashed from under the leaves. </li></ul></ul><ul><li>3 measures: </li></ul><ul><ul><li>Comprehension latency </li></ul></ul><ul><ul><li>Typing latency </li></ul></ul><ul><ul><li>Ratings of paraphrases </li></ul></ul>
  18. 18. Experiment 2: Method
  19. 19. Experiment 2: Results
  20. 20. Experiment 2: Results
  21. 21. Experiment 2: Results
  22. 22. Experiment 2: Results
  23. 23. Conclusions <ul><li>Form-meaning relationships can benefit from computational methods </li></ul><ul><ul><li>Detecting semantic clusters </li></ul></ul><ul><ul><li>Criterion for evaluating form-meaning links </li></ul></ul><ul><li>Speakers use phonaesthemes to disambiguate unfamiliar words in sentence contexts </li></ul><ul><li>Disambiguation is easier when the context coheres with the phonaestheme’s semantic contribution </li></ul>
  24. 24. Future Directions <ul><li>Spoken language more appropriate for sound similarity studies? </li></ul><ul><ul><li>BNC Spoken corpus analysis </li></ul></ul><ul><ul><li>Experiments using audible stimuli </li></ul></ul>

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