Show HN: Languagecrunch – NLP server Docker image

Sentence detection, tokenization
Sentiment
sentence: “RT @Slate: Donald Trump’s administration: “Government by the worst men.””,
sentiment: {
polarity: -1,
subjectivity: 1
},
….. // removed for brevity
entities: [
{
text: “Donald Trump’s administration”,
label: “PERSON”
}
]

Entity extraction
PERSON
NORP
FACILITY
ORG
GPE
LOC
PRODUCT
EVENT
WORK_OF_ART
LAW
LANGUAGE
DATE
TIME
PERCENT
MONEY
QUANTITY
ORDINAL
CARDINAL

Sentence type – assertive/interrogative/exclamatory/negative

Relation extraction

Eg: The currency of India is Rupees.
{
subject: “The currency”,
object: “India”,
relation: “GPE”
}

Eg: Apple is looking at buying U.K. startup for $1 billion
relations: [
{
subject: “N/A”,
object: “U.K. startup”,
relation: “GPE”
},
{
subject: “buying”,
object: “$1 billion”,
relation: “MONEY”
}
],
Word look up
Category of word – Hypernyms – colour is a hypernym of red.
Specific words of a category – Holonyms – red is a holonym of color
Synonyms to match
Examples
Word frames ( how the word is used )

Coreference resolution
Pronouns/references to nouns


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