从邀请文本中检测时间，日期和地点的算法 [英] algorithm to detect time, date and place from invitation text

问题描述

我正在研究一些自然语言处理算法来阅读一段文本，如果该文本似乎在试图建议会议要求，它将自动为您设置该会议.

I am researching some Natural Language Processing algorithms to read a piece of text, and if the text seems to be trying to suggest a meeting request, it sets up that meeting for you automatically.

例如，如果电子邮件文本显示为:

For example, if an email text reads:

让我们明天在市区晚上7点的某个地方".

Let's meet tomorrow someplace in Downtown at 7pm".

该算法应该能够检测事件的时间，日期和地点.

The algorithm should be able to detect the Time, date and place of the event.

有人知道我可以为此目的使用一些已经存在的NLP算法吗?我一直在研究一些NLP资源(例如 NLTK 和

Does someone know of some already existing NLP algorithms that I could use for this purpose? I have been researching some NLP resources (like NLTK and some tools in R), but did not have much success.

谢谢

推荐答案

这是信息提取，并且可以使用隐马尔可夫模型(HMM)或条件随机场(CRF)等序列分割算法进行更具体的解决.

This is an application of information extraction, and can be solved more specifically with sequence segmentation algorithms like hidden Markov models (HMMs) or conditional random fields (CRFs).

对于软件实施，您可能要从UMass-Amherst的 MALLET工具包开始，这是一个流行的库，它实现了用于信息提取的CRF.

For a software implementation, you might want to start with the MALLET toolkit from UMass-Amherst, it's a popular library that implements CRFs for information extraction.

您将把句子中的每个标记视为要用您感兴趣的字段标记的内容(或以上都不是'x')，作为单词特征的函数(例如词性，大写字母，字典)成员资格等).类似这样的内容:

You would treat each token in a sentence as something to be labeled with the fields you are interested in (or 'x' for none of the above), as a function of word features (like part of speech, capitalization, dictionary membership, etc.)... something like this:

token       label       features
-----------------------------------
Let         x           POS=NNP, capitalized
's          x           POS=POS
meet        x           POS=VBP
tomorrow    DATE        POS=NN, inDateDictionary
someplace   x           POS=NN
in          x           POS=IN
Downtown    LOCATION    POS=NN, capitalized
at          x           POS=IN
7pm         TIME        POS=CD, matchesTimeRegex
.           x           POS=.

不过，您将需要首先提供一些带有手工标记的培训数据.

You will need to provide some hand-labeled training data first, though.

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