1. LING/C SC/PSYC 438/538
Lecture 23
Sandiway Fong

2. Today's Topics Natural language parsing: syntactic analysis
Homeworks 11 and 12

3. Natural Language Parsing
Syntax trees are a big deal in NLP
Reminder: reading homework: JM:
chapter 5, sections 1 and 2
chapter 12
Stanford Parser / Berkeley Parser (Context-Free grammars: type-2)
Uses probabilistic rules learnt from a Treebank corpus
Output: syntax trees diagrams (also dependency graph: Stanford)
We do a lot with Treebanks in the follow-on course to this one (LING 581, Spring)

4. Natural Language Parsing
A new generation of "deep learning" parsers (last two years):
Google Cloud Natural Language (aka syntaxnet)
UDPipe
Output: dependency parses (only)

5. Training Data Penn Treebank: parsed by human annotators
Efforts by the Hong Kong Futures Exchange to introduce a new interest-rate futures contract continue to hit snags despite the support the proposed instrument enjoys in the colony’s financial community. (WSJ section)

6. Natural Language Parsing

7. Natural Language Parsing
Comparison between human parse and machine parse:
empty categories not recovered by parsing, otherwise a good match!

8. Natural Language Parsing

9. Part of Speech (POS) JM Chapter 5 Parts of speech
Classic eight parts of speech:
e.g. englishclub.com =>
traced back to Latin scholars, back further to ancient Greek (Thrax)
not everyone agrees on what they are ..
The textbook lists:
open class 4 (noun, verbs, adjectives, adverbs)
closed class 7 (prepositions, determiners, pronouns, conjunctions, auxiliary verbs, particles, numerals)
or what the subclasses are
e.g. what is a Proper Noun?
Saturday, April
Textbook answer below …

10. Part of Speech (POS) Getting POS information about a word dictionary
In computational linguistics, the Penn Treebank tagset is the most commonly used tagset (reprinted inside the front cover of your textbook)
Getting POS information about a word
dictionary
pronunciation: e.g. are you conTENT with the CONtent of the slide?
possible n-gram sequences
e.g. *pronoun << common noun
the << common noun
structure of the sentence/phrase (Syntax)
possible inflectional endings:
e.g. V-s/-ed/-en/-ing
e.g. N-s
45 tags listed in textbook
36 POS + 10 punctuation
Task: POS tagging

11. Part of Speech (POS)
NNP
NNPS

12. Part of Speech (POS)
PRP
PRP$

13. Part of Speech (POS)

14. Part of Speech (POS) Stanford parser: walk noun/verb Disambiguation:
Syntax
Bigram sequence:
*PRP << NN
DT << NN

15. Part of Speech (POS)
Word sense disambiguation (WSD) is more than POS tagging:
different senses of the noun bank

16. Syntax
Words combine recursively with one another into phrases (aka constituents)
usually when two words combine, one word will head the phrase
e.g [VB/VBP eat] [NN chocolate]
e.g [VB/VBP eat] [DT some][NN chocolate]
projects
Warning:
terminology and parses in computational linguistics
not necessarily the same as those used in theoretical linguistics
object
projects

17. Syntax
Words combine recursively with one another into phrases (aka constituents)
e.g. [PRP we][VB/VBP eat] [NN chocolate]
e.g. [TO to][VB/VBP eat] [NN chocolate]
subject

18. Syntax
Words combine recursively with one another into phrases (aka constituents)
e.g. [NNP John][VBD noticed][IN/DT/WDT that][PRP we][VB/VBP eat] [NN chocolate]
selects/subcategorizes for CP
projects
projects
preposition
complementizer (C)

19. Syntax How about a SBAR node?
Words combine recursively with one another into phrases (aka constituents)
How about a SBAR node?
PRO
cf. John wanted me to eat chocolate

20. Syntax
Words combine recursively with one another into phrases (aka constituents)
John noticed that we eat chocolate
John noticed we eat chocolate

21. Homework 11
Question 1: write a Prolog CFG for the following sentences:
John ate (sensibly) (intransitive eat)
I fish (intransitive fish)
I ate fish (transitive eat)
Bill ate rice
Harry ate roast beef
Note: you can use lowercase names… (or quotes, e.g. 'John')
Note: use Penn Treebank tagset for words (see inside the cover of your textbook, or Stanford Parser)
nnp(prp(i)) --> [i].
nnp(nnp(john)) --> [john].
vbd(vbd(ate)) --> [ate].
Your grammar should produce one parse tree per example
Your grammar should not contain infinite loops
Use ; (for more answers) to show your code obeys the aforementioned constraints
Submit your grammar and examples of runs

22. Homework 11

23. Homework 11 Question 2: expand your grammar to handle these sentences:
I ate fish, and Bill ate rice
*I ate fish, Bill ate rice
I ate fish, Bill ate rice, and Harry ate roast beef
Note: the comma can be a quoted terminal, e.g. [',']
comma(comma(',')) --> [','].
','(','(',')) --> [','].
Note: be careful of left recursion on S
(Stanford Parser)

24. Homework 12 Mandatory for 538; Extra Credit for 438.
From Ross (1970), English exhibits (forward) gapping:
I ate fish, Bill rice, and Harry roast beef
cf. I ate fish, Bill ate rice, and Harry ate roast beef
Forwards only (cf. Japanese: backwards):
I ate fish, Bill ate rice, and Harry roast beef
*I fish, Bill rice, and Harry ate roast beef
*I fish, Bill ate rice, and Harry ate roast beef
*I fish, Bill ate rice, and Harry roast beef
Parallelism requirement:
*I ate fish, Bill, and Harry roast beef
*I ate fish, Bill rice, and Harry

25. Homework 12 Gapping: I ate fish, Bill rice, and Harry roast beef
(not as gapping)
I ate fish, Bill rice, and roast beef
I ate fish, rice, and Harry roast beef (you don't have to handle these two)
Update your grammar in Homework 11 to handle gapping
HInt 1: use an extra argument to represent and spread the elided verb
Hint 2: can insert Prolog code into rules
e.g. {nonvar(V)}, {var(V)}, or {A=B}

26. Homework 12

27. Homework 12

28. Homeworks 11 and 12 Homework 11 due next Monday
Homework 12 due next Wednesday
Submit two files with each homework
PDF writeup
Your .pl file (code)