网页电话/手机号码识别

识别网页上的电话号码，一个比较容易想到的方法就是，通过预先设计电话号码的正则表达式，对网页文本内容中电话号码进行匹配，抽取出对应的联系方式。然而，这种方法是假定电话号码都是按照比较理想的格式在网页上展示的，自然对于这样的识别精度会很高，但是同时也漏掉了很多电话号码。如果你没有深入分析处理过Web网页数据，你是想象不到互联网上网页的格式到底有多不规范。

这里，我们实现一种识别网页上电话号码的方法，不需要设计精确的正则表达式来匹配电话号码，而是通过电话号码最抽象的特征来考虑和设计。

电话号码一定是一个含有数字的序列，而且可能数字之间通过一些特殊或常见的字符来进行分隔，比如“逗号”、“短线”、“空格”、“字母”等等。我们通过对一个页面的文本内容进行分析，将放宽数字字符串的定义：

如果两个数字字符之间连续，则认为两个数字字符属于同一个序列；如果两个数字字符之间存在小于给定阈值限制个数的非数字字符，则认为这两个数字字符也属于同一个序列。这种观点的实质是，将距离比较近的数字字符串合并为一个独立的序列，这样，通过分析一个页面的文本内容就可以得到一个数字字符序列的集合。

然而，这样会把比较短的数字，如日期、年龄、序号等都分析出来。自然而然想到，通过过滤算法将其过滤掉。我们这里通过一种推荐模型，计算每个数字字符序列的相似度，然后根据相似度进行排序，再从排序靠前的数字字符串序列中筛选出电话号码。

下面，看看我们用Java实现这个思路，并观察一下结果。

定义一个序列推荐接口SequenceRecommendation，recommend方法是具体的实现逻辑，可以根据自己的需要去设计。

package org.shirdrn.webmining.recommend;public interface SequenceRecommendation {public void recommend() throws Exception;
}

下面，我们实现一个用来抽取数字字符串序列的算法，并计算相关度，从而进行排序推荐。基本思路如下：

1、清洗原生的网页：将HTML标签等等都去掉，得到最终的文本内容。

2、对文本内容进行分词：使用Lucene自带的SimpleAnalyzer分析器（未使用停用词过滤），之所以选择这个是因为，在数字字符序列附近（前面和后面）存在某些具有领域特定含义的词（如电话号码数字前面和后面可能存在一些词：phone、telephone等；Email地址附近可能存在一些词：email、email us等；等等），可能它是一个停用词（对StandardAnalyzer等来说），我们不希望过滤掉这些词。另外，我们记录了每个词的位置信息。

3、聚集数字字符序列，同时记录前向和后向指定数量的词（核心）：这个应该是最核心的，需要精细地处理文本内容，和设计数据结构，得到一个我们能够方便地进行相关度计算的结果集。

4、根据一个样本集的计算结果，来建立领域模型（特征词向量），用于计算数字字符序列的相关度：我这里收集了一部分英文网页，通过英文网页的分析处理，提炼出一批特征词，为简单起见直接使用词频作为权重（注意：这样使用词频简单而且合理，也可以采用其他的方法进行权重的计算，或者补充其它属性权重的贡献）。我们这里使用了两个特征词向量，分别如下所示：

前向特征词向量（文件forwards_feature_vector）：

email                                     9124
e                                         3368
mail                                      4767
e-mail                                    2183
email us at                               178
fax                                       147
email address                             146
email us                                  121
fx                                        115
or                                        113
email us                                  102
email or                                  95
email us at                               76
or e-mail                                 67

后向特征词向量（文件backwards_feature_vector）：

phone                          27407
call                           13697
free                           13092
toll                           10092
toll free                      9012
tel                            8710
call                           5247
telephone                      4052
call us                        3108
ph                             3067
t                              2838
p                              2830
contact us                     2150
or call                        1889
local                          1477
f                              1437
or                             1362
abn                            1257
call us at                     1194
office                         1183
call us today                  1152
customer service               1101
call toll free                 1080

我们的特征词向量是通过文件形式导入，在后面的测试用例中使用。

5、相关度排序，并进行推荐：这里排序后就可一目了然，排在前面的是电话号码的可能性最大。

下面是整个思想的设计及其实现，NumberSequenceRecommendation类的代码，如下所示：

package org.shirdrn.webmining.recommend;import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.StringReader;
import java.nio.charset.Charset;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.regex.Matcher;
import java.util.regex.Pattern;import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.en.EnglishAnalyzer;
import org.apache.lucene.analysis.tokenattributes.OffsetAttribute;
import org.apache.lucene.analysis.tokenattributes.TermAttribute;
import org.apache.lucene.util.Version;
import org.jsoup.nodes.Document;
import org.jsoup.nodes.Node;
import org.jsoup.nodes.TextNode;
import org.jsoup.parser.Parser;public class NumberSequenceRecommendation implements SequenceRecommendation {private byte[] content;private Charset charset;private String baseUri;/** Max count of non-number number sequence in a continual number sequence, * on conditions of which we think the number sequence is continual.*/private int maxGap = 5;/** Max word count after or before a number sequence */private int maxWordCount = 5;private Pattern numberPattern = Pattern.compile("^\\d+$");private String cleanedContent;/** All words analyzed by Lucene analyzer from specified page text. */private LinkedList<Word> wordList = new LinkedList<Word>();private LinkedList<NumberSequence> numberSequenceList = new LinkedList<NumberSequence>();/** Final result sorted by correlation */List<NumberSequence> sortedNumberSequenceSet = new ArrayList<NumberSequence>(1);private Map<String, Double> backwardsFeatureVector = new HashMap<String, Double>();private Map<String, Double> forwardsFeatureVector = new HashMap<String, Double>();private double backwardsWeight = 1.75;private double forwardsWeight = 1.05;public NumberSequenceRecommendation() {this(new byte[]{}, Charset.defaultCharset(), null, null, null);}public NumberSequenceRecommendation(byte[] content, Charset charset, String baseUri, String backwordsFeatureVectorFile, String forwardsFeatureVectorFile) {super();this.baseUri = baseUri;this.content = content;this.charset = charset;loadFeatureVectors(backwordsFeatureVectorFile, forwardsFeatureVectorFile);}private void loadFeatureVectors(String backwordsFeatureVectorFile, String forwardsFeatureVectorFile) {load(backwordsFeatureVectorFile, backwardsFeatureVector);load(forwardsFeatureVectorFile, forwardsFeatureVector);}private void load(String featureVectorFile, Map<String, Double> featureVector) {FileInputStream fis = null;BufferedReader reader = null;try {fis = new FileInputStream(featureVectorFile);reader = new BufferedReader(new InputStreamReader(fis, charset));String line = null;while((line = reader.readLine())!=null) {if(!line.isEmpty()) {String pair[] = line.trim().split("\\s+");try {featureVector.put(pair[0].trim(), Double.parseDouble(pair[1].trim()));} catch (Exception e) { }}}} catch (FileNotFoundException e) {e.printStackTrace();}catch (IOException e) {e.printStackTrace();} finally {try {if(reader!=null) {reader.close();}if(fis!=null) {fis.close();}} catch (IOException e) {e.printStackTrace();}}       }@Overridepublic void recommend() throws Exception {recommend(content, charset, baseUri);}private List<NumberSequence> recommend(byte[] content, Charset charset, String baseUri) {String html = new String(content, charset);Document doc = Parser.parse(html, baseUri);StringBuffer buf = new StringBuffer();parseHtmlText(doc.body(), buf);cleanedContent = buf.toString().trim();collectWords(cleanedContent);analyzeNumberWords();return sortByCorrelation();}/*** Compute correlation, and sort result, for recommending.* @return*/private List<NumberSequence> sortByCorrelation() {// sort numberSequenceListfor(NumberSequence ns : numberSequenceList) {// backwardsdouble backwardsCorrelation = 0;for(Word w : ns.backwardsWords) {if(backwardsFeatureVector.containsKey(w.text)) {backwardsCorrelation += backwardsFeatureVector.get(w.text);}}// forwardsdouble forwardsCorrelation = 0;for(Word w : ns.forwardsWords) {if(forwardsFeatureVector.containsKey(w.text)) {forwardsCorrelation += forwardsFeatureVector.get(w.text);}}ns.correlation = backwardsWeight * backwardsCorrelation + forwardsWeight * forwardsCorrelation;sortedNumberSequenceSet.add(ns);}// sort by correlationCollections.sort(sortedNumberSequenceSet, new Comparator<NumberSequence>() {@Overridepublic int compare(NumberSequence o1, NumberSequence o2) {if(o1.correlation<o2.correlation) {return 1;} else if(o1.correlation>o2.correlation) {return -1;}return 0;}});return sortedNumberSequenceSet;}/*** Extract text data from a HTML page.* @param node* @param buf*/private void parseHtmlText(Node node, StringBuffer buf) {List<Node> children = node.childNodes();if(children.isEmpty() && node instanceof TextNode) {String text = node.toString().trim();for(String ch : ESCAPE_SEQUENCE) {text = text.replaceAll(ch, "");}if(!text.isEmpty()) {buf.append(text.toLowerCase().trim()).append("\n");}} else {for(Node child : children) {parseHtmlText(child, buf);}}}/*** Analyze text, extract terms by Lucene analyzer.* @param content*/private void collectWords(String content) {StringReader reader = new StringReader(content);Analyzer a = new EnglishAnalyzer(Version.LUCENE_36);TokenStream ts = a.tokenStream("", reader);TermAttribute ta = ts.addAttribute(TermAttribute.class);OffsetAttribute oa = ts.addAttribute(OffsetAttribute.class);Pos pos = new Pos();try {while(ts.incrementToken()) {Pos nextPos = new Pos(oa.startOffset(), oa.endOffset());nextPos.gap = nextPos.startOffset - pos.endOffset;Word word = new Word(ta.term(), nextPos);wordList.addLast(word);pos = nextPos;// is number?Matcher m = numberPattern.matcher(word.text);if(m.find()) {word.isNumber = true;}}} catch (IOException e) {e.printStackTrace();}}/*** Compute number words relations.*/private void analyzeNumberWords() {for(int i=0; i<wordList.size(); i++) {Word w = wordList.get(i);if(w.isNumber) {NumberSequence ns = new NumberSequence();ns.numberWords.add(w);// compute backwards wordsfor(int j=Math.max(0, i-1); j>=Math.max(i-maxWordCount, 0); j--) {if(!wordList.get(j).isNumber) {ns.backwardsWords.add(wordList.get(j));}}// recognize nearest number string sequenceint gap = 0;if(i<wordList.size()) {for(int k=i+1; ; k++) {if(gap==0) {gap = wordList.get(k).pos.gap;}if(gap<=maxGap) {if(wordList.get(k).isNumber) {ns.numberWords.add(wordList.get(k));gap = 0;} else {i = k-1;break;}ns.pos.gap += wordList.get(k).pos.gap;} else {i = k-1;break;}}// compute forwards wordsfor(int p=Math.min(i, wordList.size()-1); p<=Math.min(wordList.size(), i+maxWordCount); p++) {if(!wordList.get(p).isNumber) {ns.forwardsWords.add(wordList.get(p));}}numberSequenceList.add(ns);}}}}private static String[] ESCAPE_SEQUENCE = new String[] {""", "&", "—", "–", "‰"," ", " ", " ", " ", "‌", "‍","‚", "˜", "ˆ", "‎", "‏","×", "÷", "“", "”", "„", "<", ">", "‹", "›", "‘", "’","¡", "¢", "£", "¤", "¥", "¦", "§", "¨", "©", "ª", "«", "¬","", "®", "¯", "°", "±", "²","³", "´", "µ", "¶", "·", "¸","¹", "º", "»", "¼", "½", "¾","¿", "À", "Á", "ˆ", "Ã", "Ä","˚", "Æ", "Ç", "È", "É", "Ê","Ë", "Ì", "Í", "Î", "Ï", "Ð","Ñ", "Ò", "Ó", "Ô", "Õ", "Ö","×", "Ø", "Ù", "Ú", "Û", "Ü","Ý", "Þ", "ß", "à", "á", "â","ã", "ä", "å", "æ", "ç", "è","é", "ê", "ë", "ì", "í", "î","ï", "&ieth;", "ñ", "ò", "ó", "ô","õ", "ö", "÷", "ø", "ù", "ú","û", "ü", "ý", "ÿ"}; /*** Number sequence who holds:* <pre>* a number {@link Word} list which we analyzed from text of a page* a correlation index* a forwards {@link Word} list * a backwards {@link Word} list* a {@link Pos} which specifies this number sequence's position information* </pre>* @author shirdrn*/public static class NumberSequence {/** This sequence's position metadata */Pos pos = new Pos();/** Number word collection */List<Word> numberWords = new LinkedList<Word>();/**  */List<Word> forwardsWords = new LinkedList<Word>();List<Word> backwardsWords = new LinkedList<Word>();double correlation;@Overridepublic String toString() {return "[" +"correlation=" + correlation + ", " +"numberWords=" +numberWords + ", " +"forwardsWords=" + forwardsWords + ", " +"backwardsWords=" + backwardsWords + ", " + "]";}}/*** Word unit analyzed by Lucene's {@link Analyzer}. Here* a {@link Word} is minimum and is not split again. * @author shirdrn*/static class Word {/** Word text */String text;/** Is this word a number? */boolean isNumber;/** Word's position metadata */Pos pos;public Word(String text, Pos pos) {super();this.text = text;this.pos = pos;}@Overridepublic String toString() {return "[" +text + pos + "]";}}/*** Position information* @author shirdrn*/static class Pos {/** Start offset of a word */int startOffset;/** End offset of a word */int endOffset;/** Max distance between tow word */int gap;public Pos() {super();}public Pos(int startOffset, int endOffset) {super();this.startOffset = startOffset;this.endOffset = endOffset;}@Overridepublic String toString() {return "<" + startOffset + ", " + endOffset + ", " + gap + ">";}}public List<NumberSequence> getSortedNumberSequenceSet() {return sortedNumberSequenceSet;}public String getCleanedContent() {return cleanedContent;}
}

结果输出，包括原生网页清理后的网页文本内容，如下：

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最后，计算结果只是给出了排序的结果，可以直接观察排序推荐的效果，如下所示：

[correlation=57696.8, numberWords=[[404<6705, 6708, 3>], [878<6710, 6713, 2>], [2276<6714, 6718, 1>]], forwardsWords=[[fax<6719, 6722, 1>], [email<6739, 6744, 1>]], backwardsWords=[[phone<6697, 6702, 1>], [ga<6688, 6690, 2>], [atlanta<6679, 6686, 1>], [center<6672, 6678, 1>]], ]
[correlation=57542.45, numberWords=[[404<6725, 6728, 3>], [827<6730, 6733, 2>], [1995<6734, 6738, 1>]], forwardsWords=[[email<6739, 6744, 1>], [copyrightag<6746, 6760, 2>], [turner.com<6761, 6771, 1>], [ani<6776, 6779, 5>], [question<6780, 6789, 1>]], backwardsWords=[[fax<6719, 6722, 1>], [phone<6697, 6702, 1>]], ]
[correlation=154.35, numberWords=[[30303<6691, 6696, 1>]], forwardsWords=[[phone<6697, 6702, 1>], [fax<6719, 6722, 1>]], backwardsWords=[[ga<6688, 6690, 2>], [atlanta<6679, 6686, 1>], [center<6672, 6678, 1>], [cnn<6668, 6671, 1>], [on<6664, 6667, 1>]], ]
[correlation=0.0, numberWords=[[17<5371, 5373, 2>]], forwardsWords=[[u.s.c<5374, 5379, 1>], [physic<5390, 5398, 5>], [electron<5402, 5412, 4>], [signatur<5413, 5422, 1>]], backwardsWords=[[act<5366, 5369, 1>], [copyright<5356, 5365, 1>], [millennium<5345, 5355, 1>], [digit<5337, 5344, 8>], [act<5326, 5329, 1>]], ]
[correlation=0.0, numberWords=[[512<5382, 5385, 3>]], forwardsWords=[[physic<5390, 5398, 5>], [electron<5402, 5412, 4>], [signatur<5413, 5422, 1>], [person<5428, 5434, 6>], [author<5435, 5445, 1>]], backwardsWords=[[u.s.c<5374, 5379, 1>], [act<5366, 5369, 1>], [copyright<5356, 5365, 1>], [millennium<5345, 5355, 1>]], ]
[correlation=0.0, numberWords=[[2010<7239, 7243, 1>]], forwardsWords=[[cabl<7244, 7249, 1>], [new<7250, 7254, 1>], [network<7255, 7262, 1>], [turner<7264, 7270, 2>], [broadcast<7271, 7283, 1>]], backwardsWords=[[transcript<7227, 7238, 3>], [hln<7221, 7224, 3>], [tv<7216, 7218, 1>], [cnn<7212, 7215, 9>], [expans<7194, 7203, 1>]], ]

我们分析解释一下：

numberWords是最终的数字字符串的集合（都是数字）；

forwardsWords是对应numberWords所表示的数字字符序列前向词集合；

backwardsWords是对应numberWords所表示的数字字符序列后向词集合。

上面结果，格式化一下，便容易看出来：

[correlation=57696.8, numberWords=[404-878-2276], forwardsWords=[fax, email], backwardsWords=[phone, ga, atlanta, center]]
[correlation=57542.45, numberWords=[404-827-1995], forwardsWords=[email, copyrightag, turner.com, ani, question], backwardsWords=[fax, phone]]
[correlation=154.35, numberWords=[30303], forwardsWords=[phone, fax], backwardsWords=[ga, atlanta, center, cnn, on]]
[correlation=0.0, numberWords=[17], forwardsWords=[u.s.c, physic, electron, signatur], backwardsWords=[act, copyright, millennium, digit, act]]
[correlation=0.0, numberWords=[512], forwardsWords=[physic, electron, signatur, person, author], backwardsWords=[u.s.c, act, copyright, millennium]]
[correlation=0.0, numberWords=[2010], forwardsWords=[cabl, new, network, turner, broadcast], backwardsWords=[transcript, hln, tv, cnn, expans]]

根据上面得到的网页文本内容可以看出，第一条得分最高，确实就是电话号码，第二条是传真号码。

最后，如果我们想要使得到的电话号码更加精确，可以通过多种方式进行筛选和验证，在一定程度上会提高识别出的电话号码的精度。

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