機械翻訳を使用して、文章の非常に圧縮されたバージョンを取得できますか。私は本当においしいおいしいコーヒーを飲みたいです I want coffeeに翻訳され ます NLPエンジンのいずれかがそのような機能を提供しますか?
4037 次
4 に答える
10
その文から重要なアイデアを失うことなく文を簡潔にすることが意図されている場合は、トリプレットの主語-述語-目的語を抽出するだけでそれを行うことができます。
ツール/エンジンについて言えば、Stanford NLP を使用することをお勧めします。その依存関係パーサーの出力は、すでにサブジェクトとオブジェクト (存在する場合) を提供しています。ただし、目的の結果を得るには、まだいくつかの調整を行う必要があります。
ここからスタンフォード NLP をダウンロードして、サンプルの使用法を学ぶことができます。
あなたの質問に関連する論文を見つけました。Typed Dependencies を使用したテキストの簡素化: 異なる世代戦略のロバスト性の比較をご覧ください。
于 2012-01-09T12:46:59.490 に答える
2
これが私が見つけたものです：
Clarke と Lapata による 2008 年の「文圧縮のグローバル推論: 整数線形計画法のアプローチ」で説明されているモデルの修正された実装。
論文: https://www.jair.org/media/2433/live-2433-3731-jair.pdf
出典: https://github.com/cnap/sentence-compression (JAVAで書かれています)
入力: キャンプでは、反乱軍は「おかえりなさい」と書かれた横断幕で歓迎されました。
出力: キャンプでは、軍隊が歓迎されました。
更新: テキスト要約のためのアテンション モデルを使用したシーケンス ツー シーケンス。
于 2015-04-24T05:45:16.840 に答える
0
「ストップ ワードの削除」と「ステミングと見出し語化」を組み合わせて使用できます。ステミングとレンマタイゼーションは、テキスト内のすべての単語を基本的なルートに戻すプロセスです。詳細な説明はこちらでご覧いただけます。Porter ステマーを使用して Google で検索しています。ステミングとレンマタイゼーションの後、ストップワードの削除は非常に簡単です。これが私のストップ削除方法です。
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"};
私のプロジェクトでは、段落をテキスト入力として使用しました。
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;
}
ここからダウンロードできるスタンフォード NLP ライブラリを使用しました。何らかの形でお役に立てば幸いです。
于 2015-03-08T19:04:17.840 に答える