Overview
Language enables you to analyze text to extract meta-data from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, semantic roles, using natural language understanding and identify industry/domain specific entities and relations in unstructured text.
Beginner's Guide
Computers are great at working with structured data like spreadsheets and database tables. But us humans usually communicate in words, not in tables. That’s unfortunate for computers.A lot of information in the world is unstructured — raw text in English or another human language. How can we get a computer to understand unstructured text and extract data from it?
NLP is short for natural language processing while NLU is the shorthand for natural language understanding. Similarly named, the concepts both deal with the relationship between natural language (as in, what we as humans speak, not what computers understand) and artificial intelligence.
They share a common goal of making sense of concepts represented in unstructured data, like language, as opposed to structured data like statistics, actions, etc. To that end, NLP and NLU are opposites of a lot of other data mining techniques. But that’s where the comparisons stop: NLU and NLP aren’t the same thing.
Quickstart
Following : 5 services/APIs are provided by Language as part of Release 2.0.0.
POS Tagger : Part Of Speech tagging for a piece of text.
Sentiment Analysis (English) : Analyses the sentiment of the text and returns if it falls under positive, neutral or negative sentiment.
NER Tagger : Named Entity Recognition Tagging for a piece of text.
Dependency Parser : Dependency Parser represents the Grammatical Structure of the Sentence.
Tags : Tags Extractor for extracting keywords from a paragraph.
APIs
All URIs below are relative to https://studio.spotflock.com
| POS Tagger | POST /api/v1/language-service/phoenix-language/nlp/pos/ |
| Sentiment Analysis | POST /api/v1/language-service/phoenix-language/nlp/sentiment/ |
| Named Entity Recognition | POST /api/v1/language-service/phoenix-language/nlp/ner/ |
| Dependency Parser | POST /api/v1/language-service/phoenix-language/nlp/dependency-parser/ |
| Tags | POST /api/v1/language-service/phoenix-language/nlp/tags/ |
POS Tagger
Description
This API would enable you to do a part of speech tagging of the text.
URI
POST /api/v1/language-service/phoenix-language/nlp/pos
Headers
| api-key | Your App's API Key |
Attributes
| text | Text Sentence (String) |
Request Example
{
"text": "He did a terrific job at the construction site"
}
Response
{
"text": "He did a terrific job at the construction site.",
"result": {
"the": "DT",
"a": "DT",
"site": "NN",
"at": "IN",
"terrific": "JJ",
"construction": "NN",
"job": "NN",
"He": "PRP",
"did": "VBD"
}
}Sentiment Analysis
Description
This API would enable you to do a sentiment analysis and classify if the text is positive,neutral or negative.
URI
POST /api/v1/language-service/phoenix-language/nlp/sentiment
Headers
| api-key | Your App's API Key |
Attributes
| text | Text Sentence (String) |
Request Example
{
"text": "He has been always cheerful ever since he moved to Hyderabad."
}
Response
{
"scores": {
"neu": 0.741,
"pos": 0.259,
"compound": 0.5423,
"neg": 0
},
"text": "He has been always cheerful ever since he moved to Hyderabady",
"emotion": "POSITIVE",
"polarity": 3
}NER Tagger
Description
This API would enable you to do a named entity recognition tagging of the text. This API also finds nearest match for the person detected and gives information such as occupation, nationality of the person.
URI
POST /api/v1/language-service/phoenix-language/nlp/ner
Headers
| api-key | Your App's API Key |
Attributes
| text | Text Sentence (String) |
Request Example
{
"text": "Madan Puri bought 300 shares of Acme Corp. in 2006."
}
Response
{
"result": {
"300": "CARDINAL",
"2006": "DATE",
"Madan Puri": "PERSON",
"Acme Corp.": "ORG"
},
"persons": [
{
"Link": "https://en.wikipedia.org/wiki/Madan_Puri",
"Name": "Madan Puri",
"Nationality": "Indian",
"Occupation": "Actor"
}
],
"text": "Madan Puri bought 300 shares of Acme Corp. in 2006."
}Dependency Parser
Description
This API would enable you to do dependency-parser
URI
POST /api/v1/language-service/phoenix-language/nlp/dependency-parser/
Headers
| api-key | Your App's API Key |
Attributes
| text | Text Sentence (String) |
Request Example
{
"text": "He is a cheerful guy."
}
Response
{
"cheerful": {
"children": [],
"headPOS": "NOUN",
"dep": "amod",
"headText": "leader"
},
"is": {
"children": [
"He",
"leader",
"."
],
"headPOS": "VERB",
"dep": "ROOT",
"headText": "is"
},
"He": {
"children": [],
"headPOS": "VERB",
"dep": "nsubj",
"headText": "is"
},
".": {
"children": [],
"headPOS": "VERB",
"dep": "punct",
"headText": "is"
},
"a": {
"children": [],
"headPOS": "NOUN",
"dep": "det",
"headText": "leader"
},
"leader": {
"children": [
"a",
"cheerful"
],
"headPOS": "VERB",
"dep": "attr",
"headText": "is"
}
}Tags Extractor
Description
This API would enable you get tags from a paragraph. Note that
URI
POST /api/v1/language-service/phoenix-language/nlp/tags/
Headers
| api-key | Your App's API Key |
Attributes
| text | Text Sentence (String) |
Request Example
{
"text": "TweetDeck is a social media dashboard application for management of Twitter accounts. Originally an independent app, TweetDeck was subsequently acquired by Twitter Inc. and integrated into
Twitter's interface."
}
Response
{
"tags": [
"tweetdeck",
"twitter"
],
"text": "TweetDeck is a social media dashboard application for management of Twitter accounts. Originally an independent app, TweetDeck was subsequently acquired by Twitter Inc. and integrated
into Twitter's interface."
}Annotations
NER Supports below entities
| Type | Description |
| PERSON | People, including fictional. |
| NORP | Nationalities or religious or political groups. |
| LANGUAGE | Any named language. |
| QUANTITY | Measurements, as of weight or distance. |
| ORDINAL | “first”, “second”, etc. |
| CARDINAL | Numerals that do not fall under another type. |
| MONEY | Monetary values, including unit. |
| PERCENT | Percentage, including ”%“. |
| TIME | Times smaller than a day. |
| DATE | Absolute or relative dates or periods. |
| EVENT | Named hurricanes, battles, wars, sports events, etc. |
| WORK_OF_ART | Titles of books, songs, etc. |
| LAW | Named documents made into laws. |
| LOC | Non-GPE locations, mountain ranges, bodies of water. |
| PRODUCT | Objects, vehicles, foods, etc. (Not services.). |
| FAC | Buildings, airports, highways, bridges, etc. |
| ORG | Companies, agencies, institutions, etc. |
| GPE | Countries, cities, states. |
Annotations
POS TAGGER Supports below entities
| POS | Description |
| PUNCT | Punctuations |
| CD | Cardinal Number |
| SYM | Symbol |
| DT | determiner |
| NN, NNS | Noun |
| NNP, NNPS | proper noun |
| PRP | pronoun |
| UH | interjection |
| JJ, JJR, JJS, WDT | Adjective, comparitive and superlative |
| ADP | AdPosition like in, during |
| VB, VBG, VBN, VBP, VBZ | Verb |
| RB, RBR, RBS | Adverb |
| AUX | auxiliary |
| CC | conjunction |
| CCONJ | coordinating conjunction |
| IN | subordinating conjunction |
Data Usage FAQ
Apart from the pricing plans for Language where you consume by API calls, you can also use SCUs to consume Language APIs. Below are the SCUs consumed for each service.
| APIs | SCUs Consumed |
| POS Tagger | 2 |
| Sentiment Analysis | 3 |
| NER | 2 |
| Dependency Parser | 3 |
| Tags | 4 |
Release Notes
Folowing are the release notes as part of Release 2.0.0
POS Tagger tags supported are DT, NN, IN, JJ, PRP, VBD etc.,
NER Tagger tags supported are PER, ORG, LOC, DATE, GPE, PRODUCT, LANGUAGE, TIME, MONEY, QUANTITY etc.,
Sentiment Analysis is supported only in English.
Tags Extractor works on paragraphs and minimum frequency of any word to detect as keyword must be atleast two.
The Info of the person detected in NER is the nearest match.
