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Curry Guinn Dave Crist Haley Werth PowerPoint Presentation
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Curry Guinn Dave Crist Haley Werth

Curry Guinn Dave Crist Haley Werth

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Curry Guinn Dave Crist Haley Werth

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  1. A COMPARISON OF HAND-CRAFTED SEMANTIC GRAMMARS VERSUS STATISTICAL NATURAL LANGUAGE PARSING IN DOMAIN-SPECIFIC VOICE TRANSCRIPTION Curry Guinn Dave Crist Haley Werth

  2. Outline • Probabilistic language models • N-grams • The EPA project • Experiments

  3. Probabilistic Language Processing: What is it? • Assume a note is given to a bank teller, which the teller reads as I have a gub. (cf. Woody Allen) • NLP to the rescue …. • gub is not a word • gun, gum, Gus, and gull are words, but gun has a higher probability in the context of a bank

  4. Real Word Spelling Errors • They are leaving in about fifteen minuets to go to her house. • The study was conducted mainly be John Black. • 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.

  5. Letter-based Language Models • Shannon’s Game • Guess the next letter:

  6. Letter-based Language Models • Shannon’s Game • Guess the next letter: • W

  7. Letter-based Language Models • Shannon’s Game • Guess the next letter: • Wh

  8. Letter-based Language Models • Shannon’s Game • Guess the next letter: • Wha

  9. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What

  10. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What d

  11. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do

  12. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is?

  13. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word:

  14. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What

  15. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What do

  16. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What do you

  17. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What do you think

  18. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What do you think the

  19. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What do you think the next

  20. Letter-based Language Models • Shannon’s Game • Guess the next letter: • What do you think the next letter is? • Guess the next word: • What do you think the next word is?

  21. Word-based Language Models • A model that enables one to compute the probability, or likelihood, of a sentence S, P(S). • Simple: Every word follows every other word w/ equal probability (0-gram) • Assume |V| is the size of the vocabulary V • Likelihood of sentence S of length n is = 1/|V| × 1/|V| … × 1/|V| • If English has 100,000 words, probability of each next word is 1/100000 = .00001

  22. Word Prediction: Simple vs. Smart • Smarter: probability of each next word is related to word frequency (unigram) • – Likelihood of sentence S = P(w1) × P(w2) × … × P(wn) • – Assumes probability of each word is independent of probabilities of other words. • Even smarter: Look at probability given previous words (N-gram) • – Likelihood of sentence S = P(w1) × P(w2|w1) × … × P(wn|wn-1) • – Assumes probability of each word is dependent on probabilities of other words.

  23. Training and Testing • Probabilities come from a training corpus, which is used to design the model. • Overly narrow corpus: probabilities don't generalize • Overly general corpus: probabilities don't reflect task or domain • A separate test corpus is used to evaluate the model, typically using standard metrics • Held out test set

  24. Simple N-Grams • An N-gram model uses the previous N-1 words to predict the next one: • P(wn | wn-N+1 wn-N+2… wn-1 ) • unigrams: P(dog) • bigrams: P(dog | big) • trigrams: P(dog | the big) • quadrigrams: P(dog | chasing the big)

  25. The EPA task • Detailed diary of a single individual’s daily activity and location • Methods of collecting the data: • External Observer • Camera • Self-reporting • Paper diary • Handheld menu-driven diary • Spoken diary

  26. Spoken Diary • From an utterance like “I am in the kitchen cooking spaghetti”, map that utterance into • Activity(cooking) • Location(kitchen) • Text abstraction • Technique • Build a grammar • Example

  27. Sample Semantic Grammar ACTIVITY_LOCATION -> ACTIVITY' LOCATION' : CHAD(ACTIVITY',LOCATION') . ACTIVITY_LOCATION -> LOCATION' ACTIVITY' : CHAD(ACTIVITY',LOCATION') . ACTIVITY_LOCATION -> ACTIVITY' : CHAD(ACTIVITY', null) . ACTIVITY_LOCATION -> LOCATION' : CHAD(null,LOCATION') . LOCATION -> IAM LOCx' : LOCx' . LOCATION -> LOCx' : LOCx' . IAM -> IAM1 . IAM -> IAM1 just . IAM -> IAM1 going to . IAM -> IAM1 getting ready to . IAM -> IAM1 still . LOC2 -> HOUSE_LOC' : HOUSE_LOC' . LOC2 -> OUTSIDE_LOC' : OUTSIDE_LOC' . LOC2 -> WORK_LOC' : WORK_LOC' . LOC2 -> OTHER_LOC' : OTHER_LOC' . HOUSE_LOC -> kitchen : kitchen_code . HOUSE_LOC -> bedroom : bedroom_code . HOUSE_LOC -> living room : living_room_code . HOUSE_LOC -> house : house_code . HOUSE_LOC -> garage : garage_code . HOUSE_LOC -> home : house_code . HOUSE_LOC -> bathroom : bathroom_code . HOUSE_LOC -> den : den_code . HOUSE_LOC -> dining room : dining_room_code . HOUSE_LOC -> basement : basement_code . HOUSE_LOC -> attic : attic_code . OUTSIDE_LOC -> yard : yard_code .

  28. Statistical Natural Language Parsing • Use unigram, bigram and trigram probabilities • Use Bayes’ rule to obtain these probabilities: P(A|B) = P(B|A) * P(A)/ P(B) • The formula P(“kitchen”|30121 Kitchen) is computed by determining the percentage of times the word “kitchen” appears in diary entries that have been transcribed in the category 30121 Kitchen. • P(30121 Kitchen) is the probability that a diary entry is of the semantic category 30121 Kitchen. • P(“kitchen”) is the probability that “kitchen” appears in any diary entry. • Bayes’ rule can be extended to take into account each word in the input string.

  29. The Experiment • Digital Voice Recorder + Heart Rate Monitor • Heart rate monitor will beep if the rate changes by more than 15 beats per minute between measurements (every 2 minutes)

  30. Subjects

  31. Recordings Per Day

  32. Heart Rate Change Indicator Tones and Subject Compliance

  33. Per Word Speech Recognition

  34. Semantic Grammar Location/Activity Encoding Precision and Recall

  35. Word Recognition Accuracy’s Effect on Semantic Grammar Precision and Recall

  36. Statistical Processing Accuracy

  37. Word Recognition Affects Statistical Semantic Categorization

  38. Per Word Recognition Rate Versus Statistical Semantic Encoding Accuracy

  39. Time, Activity, Location, Exertion Data Gathering Platform

  40. Research Topics • Currently, guesses for the current activity and location are computed independently of each other • They are not independent! • Currently, guesses are based on the current utterance. • However, the current activity/location is not independent from previous activity/locations. • How do we fuse data from other sources (gps, beacons, heart rate monitor, etc.)?