使用机器翻译，我可以获得一个非常压缩的句子版本，例如。我真的很想喝一杯美味可口的咖啡会被翻译成我想要咖啡 是否有任何 NLP 引擎提供这样的功能？
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4 回答
10
如果你的目的是让你的句子简短而不丢失句子中的重要思想，那么你可以通过提取三元组主谓宾来做到这一点。
谈到工具/引擎，我推荐你使用斯坦福 NLP。它的依赖解析器输出已经提供了主题和对象（如果有的话）。但是您仍然需要进行一些调整才能获得所需的结果。
您可以在此处下载斯坦福 NLP 并学习示例用法
我找到了与您的问题相关的论文。看看使用类型依赖的文本简化：不同生成策略的鲁棒性的比较
于 2012-01-09T12:46:59.490 回答
2
这是我发现的：
Clarke 和 Lapata，2008 年，“句子压缩的全局推理：整数线性规划方法”中描述的模型的修改实现。
论文：https ://www.jair.org/media/2433/live-2433-3731-jair.pdf
来源：https ://github.com/cnap/sentence-compression （用JAVA写的）
输入： 在营地，叛军受到了横幅的欢迎，上面写着“欢迎回家”。
输出： 在营地，军队受到了欢迎。
更新： 用于文本摘要的带有注意力模型的序列到序列。
于 2015-04-24T05:45:16.840 回答
1
首先尝试使用 watson NaturalLanguageUnderstanding/Alchemy 库。使用它我能够从我的陈述中提取重要的关键字，例如：
输入：嘿！我的笔记本电脑屏幕有问题
输出：笔记本电脑屏幕问题硬件。
不仅是改写而且使用 NLU，您可以获得输入语句的以下详细信息，如上述语句，您可以获得以下类别的详细信息：
像“en”这样的语言、实体、概念、像“笔记本电脑屏幕”这样的关键字、带有相关性、文本、关键字情感、情绪等细节的“问题”。带有标签、相关性分数等细节的类别。带有句子、主题等细节的语义角色，动作和对象
除此之外，您还可以使用语气分析器来获得陈述的突出语气，例如恐惧、愤怒、快乐、厌恶等。
以下是 watson 库的代码示例 注意：waston 库不是免费的，但提供一个月的试用期，因此您可以从这个开始，然后一旦掌握了这些概念，然后切换到其他开源库并找出类似的库和功能
NaturalLanguageUnderstanding service = new NaturalLanguageUnderstanding(
NaturalLanguageUnderstanding.VERSION_DATE_2017_02_27,
WatsonConfiguration.getAlchemyUserName(),
WatsonConfiguration.getAlchemyPassword());
//ConceptsOptions
ConceptsOptions conceptOptions = new ConceptsOptions.Builder()
.limit(10)
.build();
//CategoriesOptions
CategoriesOptions categoriesOptions = new CategoriesOptions();
//SemanticOptions
SemanticRolesOptions semanticRoleOptions = new SemanticRolesOptions.Builder()
.entities(true)
.keywords(true)
.limit(10)
.build();
EntitiesOptions entitiesOptions = new EntitiesOptions.Builder()
.emotion(true)
.sentiment(true)
.limit(10)
.build();
KeywordsOptions keywordsOptions = new KeywordsOptions.Builder()
.emotion(true)
.sentiment(true)
.limit(10)
.build();
Features features = new Features.Builder()
.entities(entitiesOptions)
.keywords(keywordsOptions)
.concepts(conceptOptions)
.categories(categoriesOptions)
.semanticRoles(semanticRoleOptions)
.build();
AnalyzeOptions parameters = new AnalyzeOptions.Builder()
.text(inputText)
.features(features)
.build();
AnalysisResults response = service
.analyze(parameters)
.execute();
System.out.println(response);
于 2017-07-06T06:02:33.527 回答
0
您可以结合使用“去除停用词”和“词干和词形还原”。Stemming and lemmatization 是将文本中的所有单词返回到其基本词根的过程，您可以在此处找到完整的解释，我正在使用 Porter stemmer 在 google 中查找。在词干和词形还原之后，停用词的去除非常容易，这是我的停用词去除方法：
public static String[] stopwords ={"a", "about", "above", "across", "after", "afterwards", "again", "against", "all", "almost",
"alone", "along", "already", "also","although","always","am","among", "amongst", "amoungst", "amount", "an", "and",
"another", "any","anyhow","anyone","anything","anyway", "anywhere", "are", "around", "as", "at", "back","be","became",
"because","become","becomes", "becoming", "been", "before", "beforehand", "behind", "being", "below", "beside", "besides",
"between", "beyond", "bill", "both", "bottom","but", "by", "call", "can", "cannot", "cant", "co", "con", "could", "couldnt",
"cry", "de", "describe", "detail", "do", "done", "down", "due", "during", "each", "eg", "eight", "either", "eleven","else",
"elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone", "everything", "everywhere", "except", "few",
"fifteen", "fify", "fill", "find", "fire", "first", "five", "for", "former", "formerly", "forty", "found", "four", "from",
"front", "full", "further", "get", "give", "go", "had", "has", "hasnt",
"have", "he", "hence", "her", "here", "hereafter", "hereby", "herein", "hereupon", "hers", "herself",
"him", "himself", "his", "how", "however", "hundred", "ie", "if", "in", "inc", "indeed", "interest", "into",
"is", "it", "its", "itself", "keep", "last", "latter", "latterly", "least", "less", "ltd", "made", "many",
"may", "me", "meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly", "move", "much", "must",
"my", "myself", "name", "namely", "neither", "never", "nevertheless", "next", "nine", "no", "nobody", "none",
"noone", "nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on", "once", "one", "only", "onto",
"or", "other", "others", "otherwise", "our", "ours", "ourselves", "out", "over", "own","part", "per", "perhaps",
"please", "put", "rather", "re", "same", "see", "seem", "seemed", "seeming", "seems", "serious", "several", "she",
"should", "show", "side", "since", "sincere", "six", "sixty", "so", "some", "somehow", "someone", "something",
"sometime", "sometimes", "somewhere", "still", "such", "system", "take", "ten", "than", "that", "the", "their",
"them", "themselves", "then", "thence", "there", "thereafter", "thereby", "therefore", "therein", "thereupon",
"these", "they", "thickv", "thin", "third", "this", "those", "though", "three", "through", "throughout", "thru",
"thus", "to", "together", "too", "top", "toward", "towards", "twelve", "twenty", "two", "un", "under", "until",
"up", "upon", "us", "very", "via", "was", "we", "well", "were", "what", "whatever", "when", "whence", "whenever",
"where", "whereafter", "whereas", "whereby", "wherein", "whereupon", "wherever", "whether", "which", "while",
"whither", "who", "whoever", "whole", "whom", "whose", "why", "will", "with", "within", "without", "would", "yet",
"you", "your", "yours", "yourself", "yourselves","1","2","3","4","5","6","7","8","9","10","1.","2.","3.","4.","5.","6.","11",
"7.","8.","9.","12","13","14","A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z",
"terms","CONDITIONS","conditions","values","interested.","care","sure","!","@","#","$","%","^","&","*","(",")","{","}","[","]",":",";",",","<",">","/","?","_","-","+","=",
"a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z",
"contact","grounds","buyers","tried","said,","plan","value","principle.","forces","sent:","is,","was","like",
"discussion","tmus","diffrent.","layout","area.","thanks","thankyou","hello","bye","rise","fell","fall","psqft.","http://","km","miles"};
在我的项目中，我使用段落作为文本输入：
public static String removeStopWords(String paragraph) throws IOException{
Scanner paragraph1=new Scanner( paragraph );
String newtext="";
Map map = new TreeMap();
Integer ONE = new Integer(1);
while(paragraph1.hasNext()) {
int flag=1;
fixString=paragraph1.next();
fixString=fixString.toLowerCase();
for(int i=0;i<stopwords.length;i++) {
if(fixString.equals(stopwords[i])) {
flag=0;
}
}
if(flag!=0) {
newtext=newtext+fixString+" ";
}
if (fixString.length() > 0) {
Integer frequency = (Integer) map.get(fixString);
if (frequency == null) {
frequency = ONE;
} else {
int value = frequency.intValue();
frequency = new Integer(value + 1);
}
map.put(fixString, frequency);
}
}
return newtext;
}
我使用了斯坦福 NLP 库，你可以从这里下载。我希望我在某种程度上帮助了你。
于 2015-03-08T19:04:17.840 回答