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I have started learning nltk and following this tutorial. First we use the built-in tokenizer by using sent_tokenize and later we use PunktSentenceTokenizer. The tutorial mentions that PunktSentenceTokenizer is capable of unsupervised machine learning.

So does that mean it is better than the default one? Or what is the standard of comparison among various tokenizers?

Riken Shah
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2 Answers2

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Looking at the source code for sent_tokenize() reveals that this method currently uses the pre-trained punkt tokenizer, so it is the equivalent to PunktSentenceTokenizer. Whether or not you will need to retrain your tokenizer depends on the nature of the text you are working with. If it is nothing too exotic, like newspaper articles, then you will likely find the pre-trained tokenizer to be sufficient. Tokenizing boils down to a categorization task, and thus different tokenizers could be compared by using the typical metrics such as precision, recall, f-score etc. on labelled data.

The punkt tokenizer is based on the work published in the following paper:

http://www.mitpressjournals.org/doi/abs/10.1162/coli.2006.32.4.485#.V2ouLXUrLeQ

It is fundamentally a heuristic based approach geared to disambiguating sentence boundaries from abbreviations - the bane of sentence tokenization. Calling it a heuristic approach is not meant to be disparaging. I have used the built-in sentence tokenizer before and it worked fine for what I was doing, of course, my task did not really depend on accurate sentence tokenizing. Or rather, I was able to throw enough data at it where it did not really matter.

Here is an example of a question on SO where a user found the pre-trained tokenizer lacking, and needed to train a new one:

How to tweak the NLTK sentence tokenizer

The text in question was Moby Dick, and the odd sentence structure was tripping up the tokenizer. Some examples of where you might need to train your own tokenizer are social media (e.g. twitter) or technical literature with lots of strange abbreviations not encountered by the pre-trained tokenizer.

Community
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juanpa.arrivillaga
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  • This is a much better answer than mine. I rushed it. Yes, training is important for tokenizers if you are working with noisy data(eg, tweets) where the sentences may or may not be grammatically correct or structurally sound. Comparing the performance of tokenizers is an excersice you could perform if you have the inclination to do so. But do not expect the tokenizers to vary too much on **well made** sentences. – Ic3fr0g Jun 22 '16 at 08:18
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Sentences and words are often manually tokenized. There exist various corpora that deal with POS tagging for words according to the sentence contexts. PunktSentenceTokenizer is employed when your data(sentences and words) needs to be trained to achieve a uniform understanding of how the words should be tagged contextually. It could be possible that the data scientist manually annotates words tags for a whole bunch of sentences and then tells the machine to learn them(supervised learning). However, PunktSentenceTokenizer employs ML algorithms to learn these tags on its own (unsupervised).You just choose which data it trains upon.

Depending on the data you are the working with, the results of sent_tokenizeand consequently word_tokenize may not be that different from PunktSentenceTokenizer. Choosing between tokenizers is left upto the data scientist but the standard is always compared against manually annotated tags(because they are the most correct tags).

Ic3fr0g
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