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我处理以下代码中表示的一些文本文件：

编码 ：

$file = file($files);
$lines = str_replace("'", '', $file);
$noMultipleSpace = removeMultipleSpaces($lines);
$fileContents = array();
foreach($noMultipleSpace as $line) {
    if (isLatin($line) && count(preg_split('/\s+/', $line)) > 25) {
        $newContent = preg_split('/\\.\\s*/', $line);
        foreach($newContent as $newsContent) {
            $pos1 = stripos($newsContent, ':');
            if ($pos1 == false && count(preg_split('/\s+/', $newsContent) > 3) && isLatin($newsContent)) {
                $fileContents[] = $newsContent;
            }
        }
        $content = implode('.', $fileContents);
    }
}​

具有以下功能：

function isLatin($string) {
 return preg_match('/^\\s*[a-z,A-Z]/', $string) > 0;
}

function removeMultipleSpaces($string){
 return preg_replace('/\s+/', ' ',$string);
}

但是，在 implode 过程中，点粘贴在下一句中。例如sentence1 .Sentence2。我的期望是sentence1. Sentence2。怎么了？谢谢你 ：）

输入是文本文件，例如：

ChengXiang Zhai
Department of Computer Science University of Illinois at Urbana Champaign

ABSTRACT
Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text 
information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news articles and
revealing research trends in scientific literature. In this paper, we study a particular TTM 
task ­ discovering and summarizing the evolutionary patterns of themes in a text stream. We
define this new text mining problem and present general probabilistic methods for solving
this problem through (1) discovering latent themes from text; (2) constructing an evolution
graph of themes; and (3) analyzing life cycles of themes. Evaluation of the proposed methods
on two different domains (i.e., news articles and literature) shows that the proposed 
methods can discover interesting evolutionary theme patterns effectively. Categories and 
Subject Descriptors: H.3.3 [Information Search and Retrieval]: Clustering General Terms: 
Algorithms Keywords: Temporal text mining, evolutionary theme patterns, theme threads, 
clustering

1.

INTRODUCTION

我只想得到重要的句子，从Temporal Text Mining (TTM)...直到effectively

4

1 回答 1

2

您的中间句子似乎有一个尾随空格，导致内爆分隔符出现。

尝试这个：

$file = file($files);
$lines = str_replace("'", '', $file);
$noMultipleSpace = removeMultipleSpaces($lines);
$fileContents = array();
foreach($noMultipleSpace as $line) {
    if (isLatin($line) && count(preg_split('/\s+/', $line)) > 25) {
        $newContent = preg_split('/\\.\\s*/', $line);
        foreach($newContent as $newsContent) {
            $pos1 = stripos($newsContent, ':');
            if ($pos1 == false && count(preg_split('/\s+/', $newsContent) > 3) && isLatin($newsContent)) {
                $fileContents[] = $newsContent;
            }
        }
        $fileContents = array_map('trim', $fileContents);
        $content = implode('.', $fileContents);
    }
}​
于 2012-10-08T21:14:03.207 回答