1. Google SyntaxNet
“Parsey McParseface and other SyntaxNet models are some of the most complex networks that we have trained with the TensorFlow framework at Google.”
Quote from:
And
Globally Normalized Transition-Based Neural Networks

2. Highlights Neural Net NLP framework for TensorFlow
Pretrained Parsey McParseface parser
Trained using old English newswire stories
20 year old Penn Treebank
Wall Street Journal Speak
Over 94% accuracy & > 600 words/second
spaCy: 92.4% at 15k words per second
linguists trained for the task agree 96-97% of the cases
Parsey has cousins now!
Thanks to open source, 40+ languages
Drill Bit: POS + Dependency Parsing
Parsey’s cousins:

3. Picture and caption from: http://www. dailymail. co

4. What SyntaxNet does not do
Coreference Resolution
Named-entity recognition (NER)
Sentiment Analysis
Many other things 

5. Coreference Resolution
From:

6. Named-entity recognition
From:

7. Sentiment Analysis https://en.wikipedia.org/wiki/Sentiment_analysis
Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation (see appraisal theory), affective state (that is to say, the emotional state of the author when writing), or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).
Could consider demoing: (Chocolate is the very best and amazing)

8. What SyntaxNet does Syntactic Parser Part-of-speech (POS) tagging
Dependency Parsing

9. Why gramm[ae]r stinks
as "Those buffalo(es) from Buffalo that are intimidated by buffalo(es) from Buffalo intimidate buffalo(es) from Buffalo."[1]
From: and
The sentence uses three distinct meanings of the word buffalo: the city of Buffalo, New York; the uncommon verb to buffalo, meaning "to bully, harass,or intimidate" or "to baffle"; and the animal, bison (often called buffalo in North America). The sentence can be phrased differently as "Those buffalo(es) from Buffalo that are intimidated by buffalo(es) from Buffalo intimidate buffalo(es) from Buffalo."[1]

10. Part-of-speech (POS) tagging
From:
This sentence is composed of words: strings of characters that are segmented into groups (e.g. "I", "saw", etc.) Each word in the sentence has a grammatical function that can be useful for understanding the meaning of language. For example, "saw" in this example is a past tense of the verb "to see". But any given word might have different meanings in different contexts: "saw" could just as well be a noun (e.g., a saw used for cutting) or a present tense verb (using a saw to cut something).

11. From: https://www. google. com/url

12. POS + Dependency Parsing
From:
This sentence is composed of words: strings of characters that are segmented into groups (e.g. "I", "saw", etc.) Each word in the sentence has a grammatical function that can be useful for understanding the meaning of language. For example, "saw" in this example is a past tense of the verb "to see". But any given word might have different meanings in different contexts: "saw" could just as well be a noun (e.g., a saw used for cutting) or a present tense verb (using a saw to cut something).

13. Stanford typed dependencies (http://nlp. stanford
From:

14. Grammars Slide directly taken from Berkeley slides:
Quote: ‘The reconstruction of a sequence of grammar productions from a sentence is called “parsing” the sentence…… It is most conveniently represented as a tree….. The parser then tries to find the most likely sequence of productions that generate the given sentence’

15. One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity. It is not uncommon for moderate length sentences - say 20 or 30 words in length - to have hundreds, thousands, or even tens of thousands of possible syntactic structures. A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context.
From:
e.g. Alice drove down the street in her car has at least two possible dependency parses:

16. From: http://brnrd.me/google-syntaxnet-sentiment-analysis/

17. From: https://explosion.ai/blog/syntaxnet-in-context

18. Installation Runs on top of TensorFlow Python 2.7
Package manager: pip/brew
Build tool: Bazel, Mock (unit testing)
Other: Swig (script bindings), protobuf (serializing data), asciitree (for drawing parse trees)

19. POS + Dependency Parsing
Default
From:
This sentence is composed of words: strings of characters that are segmented into groups (e.g. "I", "saw", etc.) Each word in the sentence has a grammatical function that can be useful for understanding the meaning of language. For example, "saw" in this example is a past tense of the verb "to see". But any given word might have different meanings in different contexts: "saw" could just as well be a noun (e.g., a saw used for cutting) or a present tense verb (using a saw to cut something).
ConLL

20. Training the SyntaxNet POS Tagger
From:

21. Transition-Based Parsing + Beam Search
Paper:
Garden path sentence: "The old man the boat".

22. Backup

24. it is critical to tightly integrate learning and search in order to achieve the highest prediction accuracy

25. Phrase structure grammar
From:

26. Other links
Other: