com.luna.baidu.api.BaiduTextApi Maven / Gradle / Ivy
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package com.luna.baidu.api;
import java.io.IOException;
import java.util.HashMap;
import com.luna.common.net.HttpUtils;
import com.luna.common.net.HttpUtilsConstant;
import com.luna.common.text.CharsetKit;
import org.apache.commons.lang3.StringUtils;
import org.apache.http.HttpResponse;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.google.common.collect.ImmutableMap;
import com.google.common.collect.Maps;
import com.luna.baidu.dto.text.TextSimilarResultDTO;
import com.luna.baidu.dto.text.TextSimnetResultDTO;
/**
* @author Luna@win10
* @date 2020/5/24 21:10
*/
public class BaiduTextApi {
private static final Logger log = LoggerFactory.getLogger(BaiduTextApi.class);
/**
* 百度文本纠错
*
* @param text
* @throws IOException
*/
public static String correction(String key, String text) {
log.info("correction start key={}, text={}", key, text);
String body = "{\"text\": \"" + text + "\"" + "}";
HttpResponse httpResponse = HttpUtils.doPost(BaiduApiConstant.HOST, BaiduApiConstant.LANGUAGE_PROCESSING,
ImmutableMap.of("Content-Type", HttpUtilsConstant.JSON),
ImmutableMap.of("access_token", key, "charset", CharsetKit.UTF_8),
body);
String s = HttpUtils.checkResponseAndGetResult(httpResponse, true);
JSONObject jsonObject = JSON.parseObject(JSON.parseObject(s).get("item").toString());
String query = jsonObject.get("correct_query").toString();
log.info("correction success query={}, text={}", query, text);
return query;
}
/**
* 文本相似度比较 短文本相似度接口用来判断两个文本的相似度得分。
*
* BOW（词包）模型=>基于bag of words的BOW模型，特点是泛化性强，效率高，比较轻量级，适合任务：输入序列的 term “确切匹配”、不关心序列的词序关系，对计算效率有很高要求；
*
* GRNN（循环神经网络）模型=>基于recurrent，擅长捕捉短文本“跨片段”的序列片段关系，适合任务：对语义泛化要求很高，对输入语序比较敏感的任务；
*
* CNN（卷积神经网络）模型=>模型语义泛化能力介于 BOW/RNN 之间，对序列输入敏感，相较于 GRNN 模型的一个显著优点是计算效率会更高些。
*
* @param key
* @param model
* @return
*/
public static TextSimnetResultDTO similarityText(String key, String text1, String text2, String model) {
log.info("similarityText start key={},text1={},text2={},model={}", key, text1, text2, model);
if (StringUtils.isEmpty(model)) {
model = "BOW";
}
HashMap textParam = Maps.newHashMap();
textParam.put("text_1", text1);
textParam.put("text_2", text2);
textParam.put("model", model);
HttpResponse httpResponse = HttpUtils.doPost(BaiduApiConstant.HOST, BaiduApiConstant.TEXT_SIMILARITY,
ImmutableMap.of("Content-Type", HttpUtilsConstant.JSON),
ImmutableMap.of("access_token", key, "charset", CharsetKit.UTF_8),
JSON.toJSONString(textParam));
String s = HttpUtils.checkResponseAndGetResult(httpResponse, true);
TextSimnetResultDTO textSimnetResultDTO = JSON.parseObject(s, TextSimnetResultDTO.class);
log.info("similarityText success key={},textSimnetResultDTO={}", key, JSON.toJSONString(textSimnetResultDTO));
return textSimnetResultDTO;
}
/**
* 词语比较 输入两个词，得到两个词的相似度结果。
*
* @param word1
* @param word2
* @return
* @throws IOException
*/
public static TextSimilarResultDTO similarityWords(String key, String word1, String word2) {
log.info("similarityWords start key={},text1={},text2={}", key, word1, word2);
HashMap wordParam = Maps.newHashMap();
wordParam.put("word_1", word1);
wordParam.put("word_2", word2);
HttpResponse httpResponse = HttpUtils.doPost(BaiduApiConstant.HOST, BaiduApiConstant.WOEDS_SIMILARITY,
ImmutableMap.of("Content-Type", HttpUtilsConstant.JSON),
ImmutableMap.of("access_token", key, "charset", CharsetKit.UTF_8),
JSON.toJSONString(wordParam));
String s = HttpUtils.checkResponseAndGetResult(httpResponse, true);
TextSimilarResultDTO similarResultDTO = JSON.parseObject(s, TextSimilarResultDTO.class);
log.info("similarityWords success key={},similarResultDTO={}", key, JSON.toJSONString(similarResultDTO));
return similarResultDTO;
}
}