In linguistics, part-of-speech tagging (POS tagging), is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context — i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.

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Description

The POS tagger included in Tint is based on the Maximum Entropy Part-of-speech Tagger included in Stanford CoreNLP.

The model provided with Tint is trained on the ISTD (Italian Stanford Dependency Treebank), released for the dependency parsing shared task of Evalita-2014 and containing 316,660 tokens. The original resource is released under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 license, therefore the resulting model cannot be used for commercial purposes.

Performances

Evaluated on the Universal Dependencies test set, the POS tagger gets 96.24% accuracy overall and 82.32% accuracy on unknown words. On a 2,3 GHz Intel Core i7 with 16 GB of memory, it can tag 80,000 token/second.

Training

The Tint module for Part-of-speech tagging relies on the corresponding module in Stanford CoreNLP. You can surf to its FAQ page for more information.

In order to retrain the POS tagger using the ISTD dataset, you need to convert the original dataset to the format accepted by the Stanford MaxentTagger. The words should be tagged by having the word and the tag separated by the underscore character. For example:

Evacuata_VERB la_DET Tate_PROPN Gallery_PROPN ._PUNCT

The ISTD dataset needs to be downloaded from the Universal Dependencies website. After that, you need to run the eu.fbk.dh.tint.resources.pos.CreateTrainingForStanfordPOS class to read the CoNLLU format and transform it to the underscore-separated format.

Command parameters:

  -c,--conll <FILE>        Output in CoNLL format
     --column <FILE>       Column for POS (default 3)
     --debug               enable verbose output
  -h,--help                display this help message and terminate
  -i,--input <FILE>        Input file
  -o,--output <FILE>       Output file
  -p,--only-pos <FILE>     Output file for pos
  -t,--only-tokens <FILE>  Output file for tokens
     --trace               enable very verbose output
  -v,--version             display version information and terminate
  -x,--text <FILE>         Output text

For example, you can run it on the training set of the Universal Dependencies by using:

java eu.fbk.dh.tint.resources.pos.CreateTrainingForStanfordPOS \
    -i /path/to/ud/it-ud-train.conllu \
    -o output.train.stanford

The column parameter can be used to choose between the universal tags (--column 3, default) or the EAGLES standard tags (--column 4).

Both the property file (italian-fast.tagger.model.props) and the resulting model (italian-fast.tagger.model) are included in the Tint distribution as resources.

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Last Published: 2018/01/04.

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