6.1. Instructor presentation
1. Why don’t word for word translations work when translating a language?
2. How does understanding context help us interpret text? Do you think that NLP
uses context?
3. Do you think that natural language programs can understand metaphors and
analogies? Does NLU “understand” the meaning of language at all?
4. Why aren’t chatterbots true natural language processing programs?
5. How did SHRDLU differ from chatterbot programs?
6. What does a script do in an NLP program?
7. How does a classic story understanding program “understand” a news story?
2. Cawsey, Alison (1998): The Essence of Artificial Intelligence – Chap. 5 (pp. 98-122)
Chap. 5 Review Questions
Instructor comment: this author includes speech recognition with natural language
processing. They have been studied separately in the history of AI, although
speech understanding is really much the same problem as natural language
understanding.
8. What problem does speech recognition have that natural language processing
doesn’t?
9. How is common sense knowledge often necessary to disambiguate sentences?
Why is this a problem for AI to accomplish?
10. How does syntax help establish the meaning of a sentence for natural
language processing?
11. How does a language parser know which words in a sentence are nouns,
verbs, or determiners?
12. In the semantics analysis phase, how are the meanings of words that are added
in this stage of processing determined? (I can’t find out how from reading this
section.)
Instructor comment: the author does not fully address how word meaning is
derived through information in the knowledge base.
13. How could the meaning of “love” be represented in a knowledge base?
14. What are pragmatics in NLP?
15. What kind of knowledge is necessary to determine correct pronoun reference?
Can parsing provide it?
3. Feldman (1999): “NLP Meets the Jabberwocky”
16. What are the different aspects of language that help to define the meaning of
sentences?
17. Why can humans interpret ambiguous language without difficulty while
natural language understanding systems find it difficult?
18. Why can’t information retrieval systems retrieve documents based on their
meaning rather than using citation ranks, word frequencies, and correlations?
19. An NLU system performs semantic analysis by looking up associated words.
Is this all that is needed for an understanding of semantics?
20. The author says that an NLP information retrieval system can use context in
the query to disambiguate search terms and eliminate irrelevant search items. She
doesn’t say how. Do you know how the context of the query could be found?
Instructor comment: this article was published in 1999, before Google became so
successful. I don’t think that Google uses Natural Language Understanding in its
search algorithms. It is a statistical beast. My nephew is a Ph.D. in statistics and
works for Google.
4. Lenat (1995): “A Large-Scale Investment in Knowledge Infrastructure”
21. Why did Lenat choose to enter specific “axioms” into his knowledge base
rather than general axioms?
Instructor comment: CYC’s knowledge base used to consist of millions of rules.
They were changed to a kind of predicate calculus to make them more efficient, I
believe, and perhaps to allow for inferencing.
22. Why doesn’t CYC use certainty factors in its axioms?
23. Why is every axiom in CYC tied to a particular context?
24. Do any of the applications Lenat envisions for CYC seem to be solvable by
other kinds of software solutions?
25. How could CYC’s knowledge be used to make characters in a role-playing
game more lifelike? How could it make them more spontaneous, unpredictable,
adaptable, and crafty?
Instructor comment: I could find no more recent article of a general nature about
CYC than this one from 1995. Lenat has seemed to be less than forthcoming
about the progress or lack of it being made by CYC.
5. Ferrucci, et al. (2010): “Building Watson – an Overview”
26. Does Watson sound like it could offer an alternative in Natural Language
Processing to CYC?
27. Why should IBM undertake projects such as Deep Blue if the technology is
not transferable to other applications?
28. What does the Natural Language Understanding component of Watson do?
29. Is Watson just a super search engine? How does it demonstrate intelligence?
30. How would Watson know that two words rhyme?
31. Watson’s IBM predecessors did not work especially well. What was the
biggest contributor to its breakthrough in performance?
32. Do you think that massive parallelism, many independent experts, and
confidence estimation will be required for success in question answering systems
from now on?
Instructor comment: many existing text processing technologies were combined
to make Watson. The overall approach used was to employ all of them to
generate multiple candidates for answers and then score the candidates with
evaluation functions. These text processors often looked for patterns in a corpus
of human sentences, or, as in the case of the final scoring function, a collection of
Jeopardy questions and answers. I wonder if this is how artificial intelligence will
get its smarts in the future – not from programming it in, but through extracting it
from humans through machine learning.
33. Do you think that the question classification system within Watson contains
specialized heuristics for answering each kind of question in Jeopardy? Will
heuristics and programming tricks always be needed in any AI system?
34. Is there any real understanding of semantics anywhere in Watson?
35. Could Watson be said to use a swarm approach to question answering, i.e., a
great number of experts generating results and communicating them to other
experts in the swarm?
36. How do you think that Watson “detects relations” between objects in a
database?
37. The question decomposition function that relies on computed confidence
scores is conceptually similar to heuristic search in problem solving. How?
38. Watson generates hundreds of hypothetical answers for each Jeopardy
question. How does it decide which one is right?
39. Can Watson be considered a Natural Language Understanding program? Why
or why not?
40. Do any of the scoring algorithms used in Watson’s hypothesis scoring have
any understanding of the meaning of the candidate answers?
41. If it takes a system like Watson with massively parallel processors, hundreds
of algorithmic routines, and a large team of developers striving for years to make
it a success at one specialized task, won’t these systems become too expensive for
general use?
42. How was machine learning used to improve Watson’s final answer scoring
function?
43. Can you think of other areas of AI that may benefit from applying the Watson
approach to AI: massively parallel processing employing a vast array of
probabilistic algorithms tuned to produce the desired output from a particular
class of inputs?
44. With reference to Searle’s Chinese Room argument, can Watson be said to
understand the Jeopardy questions it answers?
7.1. Instructor presentation:
1. What is speech synthesis?
2. Which parts of the vocal apparatus produce consonant sounds?
3. What is a “formant?”
4. Are phonemes the same as vowels or consonants? If not, what are they?
5. What is phoneme co-articulation and why is it a problem for speech
recognition?
6. What is surprising about the “Phonemic Restoration Effect?”
7. Why is “discrete” speech easiest to recognize?
8. What does statistical modeling do to improve speech recognition performance?
9. What does speech understanding try to do? Is it much different than natural
language understanding?
2. Cawsey (1998): The Essence of Artificial Intelligence – Chap. 5 (pp. 98-103)
Instructor comment: this chapter was already covered in Section 6: Natural
Language Understanding.
3. Deng and Huang (2004): Challenges in Adopting Speech Recognition
10. What do the authors attribute progress in speech recognition to over the past
30 years?
11. According to the authors, what has to be done to increase the acceptance of
speech recognition systems?
12. Why don’t speech recognition systems work well in noisy environments?
Why is this less of a problem for humans?
13. How will adding a true understanding of context and common sense improve
speech recognition?
14. Why is conversational speech difficult for speech recognition systems to
process accurately?
4. Furman, et al. (1999): Speech-Based Services
15. Why is accuracy in word recognition such a limiting factor in the user
acceptance of speech recognition systems?
16. Errors in the recognition of speech will occur. How does the “Lucy” system
try to minimize the inconvenience?
17. Why can’t word templates used to identify single words be expanded to
recognize continuous speech?
18. Do continuous speech recognition algorithms identify individual words?
19. What is “wordspotting” and what does it enable a speech recognition system
to do with connected speech?
20. How does grammar-based speech recognition reduce errors in processing
connected speech?
21. What is a “finite-state grammar map”? Why is it tedious to construct and not
generalizable across applications?
22. How does statistical language modeling work? What advantage does it have
over finite-state grammars?
23. What limitation does statistical language modeling have on the range of
utterances it can handle?
24. According to the authors, what will have to be accomplished before
“unconstrained speech” understanding is attained?
5. White (1990): Natural Language Understanding and Speech Recognition
Instructor comment: this article, like many technical articles, does not explain the
technical jargon used. Therefore, parts of the text are dense and hard to
understand for those of us unfamiliar with the research.
25. Why haven’t speech recognition systems simply been merged with Natural
Language Understanding systems to create speech understanding?
26. Why is connected speech so difficult for speech recognition?
27. Why do you think humans can recognize connected speech effortlessly, while
speech recognition systems have such difficulty?
28. What are 29. What is prosody? Can it be incorporated into speech
recognition?
30. Does this article adequately define context and how it constrains the
interpretation of speech? Why or why not?
31. What problem does ungrammatical speech pose for speech recognition?
32. How does the author envision using higher level speech knowledge
(semantics, pragmatics, prosodics) to enhance system performance at the
phoneme level?
33. Why does the author think that the ability to combine and coordinate higher
level knowledge sources through NLU merging with ASR will lead to
progress in speech recognition?