識別網頁上的電話號碼，一個比較容易想到的方法就是，通過預先設計電話號碼的正則表達式，對網頁文本內容中電話號碼進行匹配，抽取出對應的聯繫方式。然而，這種方法是假定電話號碼都是按照比較理想的格式在網頁上展示的，自然對於這樣的識別精度會很高，但是同時也漏掉了很多電話號碼。如果你沒有深入分析處理過Web網頁數據，你是想象不到互聯網上網頁的格式到底有多不規範。
這裏，我們實現一種識別網頁上電話號碼的方法，不需要設計精確的正則表達式來匹配電話號碼，而是通過電話號碼最抽象的特徵來考慮和設計。
電話號碼一定是一個含有數字的序列，而且可能數字之間通過一些特殊或常見的字符來進行分隔，比如“逗號”、“短線”、“空格”、“字母”等等。我們通過對一個頁面的文本內容進行分析，將放寬數字字符串的定義：
如果兩個數字字符之間連續，則認爲兩個數字字符屬於同一個序列；如果兩個數字字符之間存在小於給定閾值限制個數的非數字字符，則認爲這兩個數字字符也屬於同一個序列。這種觀點的實質是，將距離比較近的數字字符串合併爲一個獨立的序列，這樣，通過分析一個頁面的文本內容就可以得到一個數字字符序列的集合。
然而，這樣會把比較短的數字，如日期、年齡、序號等都分析出來。自然而然想到，通過過濾算法將其過濾掉。我們這裏通過一種推薦模型，計算每個數字字符序列的相似度，然後根據相似度進行排序，再從排序靠前的數字字符串序列中篩選出電話號碼。
下面，看看我們用Java實現這個思路，並觀察一下結果。
定義一個序列推薦接口SequenceRecommendation，recommend方法是具體的實現邏輯，可以根據自己的需要去設計。
package org.shirdrn.webmining.recommend;
public interface SequenceRecommendation {
public void recommend() throws Exception;
}
下面，我們實現一個用來抽取數字字符串序列的算法，並計算相關度，從而進行排序推薦。基本思路如下：
1、清洗原生的網頁：將HTML標籤等等都去掉，得到最終的文本內容。
2、對文本內容進行分詞：使用Lucene自帶的SimpleAnalyzer分析器（未使用停用詞過濾），之所以選擇這個是因爲，在數字字符序列附近（前面和後面）存在某些具有領域特定含義的詞（如電話號碼數字前面和後面可能存在一些詞：phone、telephone等；Email地址附近可能存在一些詞：email、email us等；等等），可能它是一個停用詞（對StandardAnalyzer等來說），我們不希望過濾掉這些詞。另外，我們記錄了每個詞的位置信息。
3、聚集數字字符序列，同時記錄前向和後向指定數量的詞（核心）：這個應該是最核心的，需要精細地處理文本內容，和設計數據結構，得到一個我們能夠方便地進行相關度計算的結果集。
4、根據一個樣本集的計算結果，來建立領域模型（特徵詞向量），用於計算數字字符序列的相關度：我這裏收集了一部分英文網頁，通過英文網頁的分析處理，提煉出一批特徵詞，爲簡單起見直接使用詞頻作爲權重（注意：這樣使用詞頻簡單而且合理，也可以採用其他的方法進行權重的計算，或者補充其它屬性權重的貢獻）。我們這裏使用了兩個特徵詞向量，分別如下所示：
前向特徵詞向量（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後向特徵詞向量（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我們的特徵詞向量是通過文件形式導入，在後面的測試用例中使用。
5、相關度排序，並進行推薦：這裏排序後就可一目瞭然，排在前面的是電話號碼的可能性最大。
下面是整個思想的設計及其實現，NumberSequenceRecommendation類的代碼，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();
}
}
}
@Override
public 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 numberSequenceList
for(NumberSequence ns : numberSequenceList) {
// backwards
double backwardsCorrelation = 0;
for(Word w : ns.backwardsWords) {
if(backwardsFeatureVector.containsKey(w.text)) {
backwardsCorrelation += backwardsFeatureVector.get(w.text);
}
}
// forwards
double 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 correlation
Collections.sort(sortedNumberSequenceSet, new Comparator<NumberSequence>() {
@Override
public 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 words
for(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 sequence
int 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 words
for(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;
@Override
public 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;
}
@Override
public 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;
}
@Override
public String toString() {
return "<" + startOffset + ", " + endOffset + ", " + gap + ">";
}
}
public List<NumberSequence> getSortedNumberSequenceSet() {
return sortedNumberSequenceSet;
}
public String getCleanedContent() {
return cleanedContent;
}
}結果輸出，包括原生網頁清理後的網頁文本內容，如下：
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help[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>]], ]
我們分析解釋一下：
numberWords是最終的數字字符串的集合（都是數字）；
forwardsWords是對應numberWords所表示的數字字符序列前向詞集合；
backwardsWords是對應numberWords所表示的數字字符序列後向詞集合。
上面結果，格式化一下，便容易看出來：
[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]]最後，如果我們想要使得到的電話號碼更加精確，可以通過多種方式進行篩選和驗證，在一定程度上會提高識別出的電話號碼的精度。