How should I use KBP with Java API?

[hshsilver]

I found useful tools to get the relation underneath the texts on page KBPAnnotator But there is no example of Java API, so I cannot directly get the result in the IDE console. Does this module support Java API? Thanks for your reply~

[J38]

Please see: https://stanfordnlp.github.io/CoreNLP/api.html

[hshsilver]

In fact, I did not find how to use Annotation data structure to get the KBP result. I have tried to add "kbp" in the property file.

`# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)
annotators = tokenize, ssplit, pos, lemma, ner, regexner, parse, coref,tokensregex, kbp

.........(omited)

kbp.semgrex = edu/stanford/nlp/models/kbp/chinese/semgrex
kbp.tokensregex = edu/stanford/nlp/models/kbp/chinese/tokensregex
kbp.language = zh
kbp.model = none
`

However, there is no result of kbp. What should I do to get the missing result?

[J38]

Make sure you are using Stanford CoreNLP 3.9.2 and the most recent Chinese properties file (which is included in the Chinese models jar). Obviously add kbp to the annotators list.

It should work on this text: 巴拉克·奥巴马是美国总统。他在2008年当选.

Note that failure on any given input text doesn't mean a wrong configuration, it is quite possibly algorithm failure which is common.

I'll post the correct properties for convenience:

# Pipeline options - lemma is no-op for Chinese but currently needed because coref demands it (bad old requirements system)
annotators = tokenize, ssplit, pos, lemma, ner, parse, coref,kbp

# segment
tokenize.language = zh
segment.model = edu/stanford/nlp/models/segmenter/chinese/ctb.gz
segment.sighanCorporaDict = edu/stanford/nlp/models/segmenter/chinese
segment.serDictionary = edu/stanford/nlp/models/segmenter/chinese/dict-chris6.ser.gz
segment.sighanPostProcessing = true

# sentence split
ssplit.boundaryTokenRegex = [.。]|[!?！？]+

# pos
pos.model = edu/stanford/nlp/models/pos-tagger/chinese-distsim/chinese-distsim.tagger

# ner
ner.language = chinese
ner.model = edu/stanford/nlp/models/ner/chinese.misc.distsim.crf.ser.gz
ner.applyNumericClassifiers = true
ner.useSUTime = false

# regexner
ner.fine.regexner.mapping = edu/stanford/nlp/models/kbp/chinese/gazetteers/cn_regexner_mapping.tab
ner.fine.regexner.noDefaultOverwriteLabels = CITY,COUNTRY,STATE_OR_PROVINCE

# parse
parse.model = edu/stanford/nlp/models/srparser/chineseSR.ser.gz

# depparse
depparse.model    = edu/stanford/nlp/models/parser/nndep/UD_Chinese.gz
depparse.language = chinese

# coref
coref.sieves = ChineseHeadMatch, ExactStringMatch, PreciseConstructs, StrictHeadMatch1, StrictHeadMatch2, StrictHeadMatch3, StrictHeadMatch4, PronounMatch
coref.input.type = raw
coref.postprocessing = true
coref.calculateFeatureImportance = false
coref.useConstituencyTree = true
coref.useSemantics = false
coref.algorithm = hybrid
coref.path.word2vec =
coref.language = zh
coref.defaultPronounAgreement = true
coref.zh.dict = edu/stanford/nlp/models/dcoref/zh-attributes.txt.gz
coref.print.md.log = false
coref.md.type = RULE
coref.md.liberalChineseMD = false

# kbp
kbp.semgrex = edu/stanford/nlp/models/kbp/chinese/semgrex
kbp.tokensregex = edu/stanford/nlp/models/kbp/chinese/tokensregex
kbp.language = zh
kbp.model = none

# entitylink
entitylink.wikidict = edu/stanford/nlp/models/kbp/chinese/wikidict_chinese.tsv.gz