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<link rel="stylesheet" type="text/css" href="https://cdn.jsdelivr.net/hint.css/2.4.1/hint.min.css"><p>最强大脑VS百度大脑最近很是热闹，于是乎发现百度大脑平台在1月06日开放了nlp自然语言处理的接口，于是试用了1.0版本。使用方法分为API和SDK两种方式。 ## API接口调用 - 首先获取access_token,需要用到API Key和Secret Key</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">public static String getAccess_token() {</div><div class="line"> List<NameValuePair> nameValuePairs = new ArrayList<NameValuePair>();</div><div class="line"> nameValuePairs.add(new BasicNameValuePair("grant_type", Message.GRANT_TYPE));</div><div class="line"> nameValuePairs.add(new BasicNameValuePair("client_id", Message.CLIENT_ID));</div><div class="line"> nameValuePairs.add(new BasicNameValuePair("client_secret", Message.CLIENT_SECRET));</div><div class="line"> String result = null;</div><div class="line"> try {</div><div class="line"> result = postNameValuePair(Message.TOKEN_URL,nameValuePairs);</div><div class="line"> } catch (IOException e) {</div><div class="line"> e.printStackTrace();</div><div class="line"> }</div><div class="line"> return result;</div><div class="line"> </div><div class="line"> }</div></pre></td></tr></table></figure>
<ul>
<li>接下来用获取的access_token（目前为1个月重新获取一次）调用相关接口即可。 ## JAVA SDK接口调用</li>
<li>首先通过API Key和Secret Key以及APP_ID创建一个客户端对象。</li>
</ul>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div></pre></td><td class="code"><pre><div class="line">public static AipNlp client = new AipNlp(Message.APP_ID, Message.CLIENT_ID, Message.CLIENT_SECRET);</div></pre></td></tr></table></figure>
<ul>
<li>接着用客户端对象调用相关接口即可。</li>
</ul>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div><div class="line">19</div><div class="line">20</div><div class="line">21</div><div class="line">22</div><div class="line">23</div><div class="line">24</div><div class="line">25</div><div class="line">26</div><div class="line">27</div><div class="line">28</div><div class="line">29</div><div class="line">30</div><div class="line">31</div></pre></td><td class="code"><pre><div class="line"> public void wordseg(String sentence) {</div><div class="line"> // 使用指定语言分词</div><div class="line"> JSONObject response = NlpUtil.client.wordseg(sentence, NlpLangId.LANG_CHINESE);</div><div class="line"> JSONObject scw_out = response.getJSONObject("scw_out");</div><div class="line"> String wordsepbuf = scw_out.getString("wordsepbuf");</div><div class="line"> String wpcompbuf = scw_out.getString("wpcompbuf");</div><div class="line"> String subphrbuf = scw_out.getString("subphrbuf");</div><div class="line"> String str[] = wpcompbuf.split("\t");</div><div class="line"> for (int i = 0; i < str.length; i++) {</div><div class="line"> System.out.println(str[i]);</div><div class="line"> }</div><div class="line"> System.out.println("基本词粒度结果:"+wordsepbuf);</div><div class="line"> System.out.println("混排粒度结果"+wpcompbuf);</div><div class="line"> System.out.println("所有识别出来的短语"+subphrbuf); </div><div class="line"> } </div><div class="line"></div><div class="line">public List<Word> wordpos(String sentence) {</div><div class="line"> JSONObject response = NlpUtil.client.wordpos(sentence);</div><div class="line"> JSONArray result_out = response.getJSONArray("result_out");</div><div class="line"> List<Word> words = new ArrayList<Word>();</div><div class="line"> Word word;</div><div class="line"> for (int i = 0; i < result_out.length(); i++) {</div><div class="line"> word = new Word();</div><div class="line"> word.setLength(result_out.getJSONObject(i).getInt("length"));</div><div class="line"> word.setConfidence(result_out.getJSONObject(i).getInt("confidence"));</div><div class="line"> word.setType(result_out.getJSONObject(i).getString("type"));</div><div class="line"> word.setWord(result_out.getJSONObject(i).getString("word"));</div><div class="line"> words.add(word);</div><div class="line"> }</div><div class="line"> return words;</div><div class="line"> }</div></pre></td></tr></table></figure>
<ul>
<li>测试分词结果： <figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div></pre></td><td class="code"><pre><div class="line">public class Test {</div><div class="line"> public static void main(String[] args) {</div><div class="line"> String sentence = "如何成为一名优雅的程序员";</div><div class="line"> WordSeg wordSeg = new WordSeg();</div><div class="line"> List<Word> words = wordSeg.wordpos(sentence);</div><div class="line"> for (Word word : words) {</div><div class="line"> System.out.println("单词："+word.getWord()+" "+"词性："+word.getType()+" "+"置信度："+word.getConfidence());</div><div class="line"> }</div><div class="line"> }</div><div class="line">}</div></pre></td></tr></table></figure></li>
</ul>
<p>结果相当准确：</p>
<div class="figure">
<img src="http://cdn.sinacloud.net/myblog-image/2017/01-16/01.PNG" alt="分词结果">
<p class="caption">分词结果</p>
</div>
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