Question

我处理下面代码中表示的一些文本文件：

代码：

$file = file($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);
    }
}

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，在内爆过程中，下一句中的点粘贴。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

我只想从Temporal Text Mining (TTM)...到effectively

获得重要的句子

Answer 1

您的中间句子似乎有一个尾随空格，导致内部分隔符显示为关闭。

试试这个：

$file = file($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);
    }
}

内爆的结果仍然不如预期

1 个答案: