如何在英语句子中检测产品的功能-NLP [英] How to detect features of a product in an english sentence - nlp

问题描述

我正在尝试用英语句子检测产品(例如分别为移动设备)的功能(例如:屏幕，处理速度).为此，我的方法是在一个包含多个句子的段落(谈论产品)中，出现频率最高的词(除了代词或好词，坏词等情感词，我将它们存储在文件中)产品的特性，因此我根据它们的频率和它们与情感词的距离进行排名，并排在它们的前n位.

I am trying to detect features(eg.: screen, processing speed) of a product(eg.: mobile, respectively) in an english sentence. For this, my approach is that in a paragraph(that talks about the product) containing multiple sentences, the words( apart from words like pronouns or sentiment words like good, bad etc, which I store in a file) that appear most frequently are the features of that product and so I rank on the basis of their frequency and their distance with the sentiment words and take teh top n of them.

但是，它不是很有效.任何人都可以提出其他更好的方法来检测产品特征词吗?

However, it is not very effective. Can anyone suggest some other and better approach for detecting the words which are features of a product?

推荐答案

该领域已进行了大量研究.从阅读刘冰在这一领域的开创性工作(Liu 2004，Liu 2005)开始.

There's been massive amount of research in this area. Start from reading Bing Liu's seminal work (Liu 2004, Liu 2005) in this area.

一种流行的技术是通过Stanford CodeNLP使用依赖图. 您可以使用 nsubj 依赖项来创建连接到形容词( JJ )的名词( NN )之类的规则.对于基本系统，这种5-10条规则就足够了.

One popular technique is using Dependency Graph using Stanford CodeNLP. You can make rules like a Noun (NN) connected to an Adjective (JJ) using nsubj dependency. 5-10 rules of this kind would be sufficient for a basic system.

该领域的最新技术使用序列标记方法(CRF/HMM)标记每个单词，无论它是否是功能术语.但是，您需要大量的标记数据.查看基于方面的情感分析

State of the art in this area uses Sequence Tagging approach (CRF/HMM) for tagging each word whether it is an feature term or not. However you need good amount of labelled data for it. Check recent works in the area of Aspect Based Sentiment Analysis.

寻求帮助的资源:

http://alt.qcri.org/semeval2015/task12/
http://www.aueb.gr/users/ion/docs/pavlopoulos_phd_thesis.pdf
http://www.aclweb.org/anthology/S14-2004

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