1. Stanford CoreNLP

2. Content Architecture Annotator Examples from QALD-5 POS Tagger NER
Parser
Dependency Parser
Coreference Resolution
Examples from QALD-5

3. Architecture
Annotator: adds some kind of analysis information to an Annotation object.
Annotation: a type safe heterogeneous map, the data structure which hold the results of annotators.

4. Annotator 16 annotators 1 TokenizerAnnotator 9 TrueCaseAnnotator 2
CleanXmlAnnotator 10
ParserAnnotator 3
WordToSentenceAnnotator 11
DependencyParseAnnotator 4
POSTaggerAnnotator 12
DeterministicCorefAnnotator 5
MorphaAnnotator 13
RelationExtractorAnnotator 6
NERClassifierCombiner 14
NaturalLogicAnnotator 7
RegexNERAnnotator 15
QuoteAnnotator 8
SentimentAnnotator 16
EntityMentionsAnnotator
NaturalLogicAnnotator: Marks quantifier scope and token polarity, according to natural logic semantics.

5. POS Tagger http://nlp.stanford.edu/software/tagger.shtml
Labels tokens with their part-of-speech (POS) tag
Maximum entropy
Support English, Arabic, Chinese, French, Spanish, and German
Accuracy:
97.24% (Penn Treebank tagset, English Penn Treebank WSJ)
93.46% (LDC Chinese Treebank POS tag set, Chinese and Hong Kong texts, % on unknown words)
Example

6. NER, regexner http://nlp.stanford.edu/software/CRF-NER.shtml
Recognizes named (PERSON, LOCATION, ORGANIZATION, MISC).
CRF sequence taggers
Support English, Dutch, Spanish, German
F % (Prec 88.21%, Rec 87.68%. CoNLL 2003 English news testb)
Model: english.muc.7class.distsim.crf.ser, english.conll.4class.distsim.crf.ser
Example
numerical (MONEY, NUMBER, DATE, TIME, DURATION, SET) entities
rule-based
Bachelor of (Arts|Laws|Science|Engineering) DEGREE

7. Parser http://nlp.stanford.edu/software/lex-parser.shtml
Syntactic analysis
Support English, Chinese, Arabic, Spanish, German
PCFG(2003), recursive neural network(2013)
F1: 86.36% (PCFG), 90.4% (RNN) (Penn Treebank WSJ Engish)
Model: englishPCFG.ser, englishRNN.ser
Example

8. DependencyParse http://nlp.stanford.edu/software/nndep.shtml
analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads.
two type of output: basic, collapsed
Support English, Chinese
Neural Network, transition-based
English Penn Treebank and Chinese Penn Treebank(2014)
Universal Dependencies representation
unlabeled attachment scores (UAS) English 92.0, Chinese 83.9
labeled attachment scores (LAS) English 90.7, Chinese 82.4
Models
english_UD.gz (default, English, Universal Dependencies) PTB_Stanford_params.txt.gz (English, Stanford Dependencies) PTB_CoNLL_params.txt.gz (English, CoNLL Dependencies)  CTB_CoNLL_params.txt.gz (Chinese, CoNLL Dependencies)

9. DependencyParse

10. Coreference Resolution http://nlp.stanford.edu/software/dcoref.shtml
pronominal and nominal coreference resolution
The music was so loud that it couldn't be enjoyed.
The project leader is refusing to help. The jerk thinks only of himself.
Support English, Chinese
Rule-based, Sieve-based
(……, DiscourseMatch, ExactStringMatch, ……, RelaxedHeadMatch, PronounMatch)
Avg F1 59.5% (CoNLL-2011 Shared Task data set, 2013)
Dictionary:
Demonym (Asia Asian Asians)
Male (johannsen johansen johanson johansson)
Female (kate katelyn kater katerina) ……
Example:

11. Examples from QALD-5(1)
Wo has vice president under the president who authorized atomic weapons against Japan during World War II?

12. Examples from QALD-5(2)
Of the people that died of radiation in Los Alamos, whose death was an accident?

13. Examples from QALD-5(3)
Which actress starring in the TV series Friends owns the production company Coquette Productions?

14. Examples from QALD-5(4)
Which city does the first person to climb all 14 eight-thousanders come from?

15. Examples from QALD-5(5)
What is the largest city in the county in which Faulkner spent most of his life?

16. Thank you!