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Ontology-Based Topic Detection

FREEMIUM
開発者​ Proxem
更新日 18時間前​
データ​
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Ontology-Based Topic Detection API Documentation

特許を取得したNLP技術により最も関連性の高いWikipediaのカテゴリを抽出することにより、テキストが何であるかを調べるテキスト分析サービス

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POSTGet categories
POSTGet corpus categories
POSTGet categories

指定されたテキストに関連付けられた上位のテーマを返します。

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ヘッダーパラメータ​
X-RapidAPI-KeySTRING
REQUIRED
AcceptSTRING
OPTIONALThe expected type of the response
必須パラメータ​
DocumentJSON_STRING
REQUIREDThe document to analyze
パラメータ(Option)​
nbtopcatNUMBER
OPTIONALThe max numbers of expected categories (max 50)
cleanupBOOLEAN
OPTIONALTry to remove the less useful categories (default to true)
srclangSTRING
OPTIONALSet the language of the given document (prevent the auto-detection)
edgesBOOLEAN
OPTIONALSet to true to receive parent/child relations between categories
コードスニペット​
unirest.post("https://proxem-thematization.p.rapidapi.com/api/wikiAnnotator/GetCategories?nbtopcat=undefined&cleanup=undefined&srclang=undefined&edges=undefined")
.header("X-RapidAPI-Key", "undefined")
.header("Accept", "undefined")
.header("Content-Type", "text/plain")
.send("At Proxem, our clients ask us to extract information from e-mails, social medias, press articles, and basically any type of text you can imagine. In the standard case, the text to process is written in various languages. To establish systems that support a wide scale of languages and formats is one of the mission of our Research team.Another goal of ours is to develop cross-lingual algorithms, that is algorithms which take as input texts in different languages and output an information computed on all those texts. For example on a task called sentiment analysis, which consists in detecting the \"polarity\" of a document (\"is this document rather positive or negative?\"), we want to implement a unique algorithm that would take as input sentences in English, Chinese, Spanish, etc and would compute a score. There are multiple reasons for us to aim at this. One is for simplicity sake. Indeed, we do not want to implement as many algorithms as languages we may have to handle. Another reason for that choice is that we want to leverage the important amount of available data for some languages to improve the accuracy on languages where data is rare.")
.end(function (result) {
  console.log(result.status, result.headers, result.body);
});
サンプルレスポンス​

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SDKを​インストール(NodeJS)​

インストール​

Node.jsで​Unirestを​使用​するには、​NPMモジュールを​インストール​してください。​

$ npm install unirest

インストール​完了後は、​簡単に​リクエストを​行うことが​できるように​なります。​

var unirest = require('unirest');

リクエスト​

unirest.post("https://proxem-thematization.p.rapidapi.com/api/wikiAnnotator/GetCategories?nbtopcat=undefined&cleanup=undefined&srclang=undefined&edges=undefined")
.header("X-RapidAPI-Key", "undefined")
.header("Accept", "undefined")
.header("Content-Type", "text/plain")
.send("At Proxem, our clients ask us to extract information from e-mails, social medias, press articles, and basically any type of text you can imagine. In the standard case, the text to process is written in various languages. To establish systems that support a wide scale of languages and formats is one of the mission of our Research team.Another goal of ours is to develop cross-lingual algorithms, that is algorithms which take as input texts in different languages and output an information computed on all those texts. For example on a task called sentiment analysis, which consists in detecting the \"polarity\" of a document (\"is this document rather positive or negative?\"), we want to implement a unique algorithm that would take as input sentences in English, Chinese, Spanish, etc and would compute a score. There are multiple reasons for us to aim at this. One is for simplicity sake. Indeed, we do not want to implement as many algorithms as languages we may have to handle. Another reason for that choice is that we want to leverage the important amount of available data for some languages to improve the accuracy on languages where data is rare.")
.end(function (result) {
  console.log(result.status, result.headers, result.body);
});
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