poslog 0.7

PosLog: A CRF-based Part-of-Speech Tagger for Log Messages

PosLog

A CRF-based Part-of-Speech (POS) Tagger for Log Messages.

Usage

Use Default Model

There are three ways to use the default model:

1. Predict the PoS tags of a list of tokens returning a list of tags.

   from poslog import PosLogTokenizer, PosLogCRF
   
   msg="Tag this sentence."
   
   tokenizer=PosLogTokenizer()
   tokens=tokenizer.tokenize(msg)
   # ['Tag', 'this', 'sentence', '.']
   
   pos_log=PosLogCRF()
   # predict(X:list[str])->list[str]
   pos_log.predict(tokens)
   # ['VERB' 'DET' 'NOUN' 'PUNCT']
   

2. Predict the PoS tags of a string returning a list of tags.

   # predict_string(X:str)->list[str]
   pos_log.predict_string(msg)
   # ['VERB' 'DET' 'NOUN' 'PUNCT']
   

3. Predict the PoS tags of a string returning a list of tuples with token and tag.

   # predict_string_as_tuple(X:str)->list[tuple[str,str]]
   pos_log.predict_string_as_tuple(msg)
   # [('Tag', 'VERB'), ('this', 'DET'), ('sentence', 'NOUN'), ('.', 'PUNCT')]
   

Train Your Own Model

Define model name in constructor:

pos_log=PosLogCRF(model_name="abs_path_to_my_model")

You can give abs_path_to_my_model as absolute path or relative path.
Note: Relative paths models will stored in package directory models/ and will be overwritten if you renew the environment.

PosLog takes training data as tokens and tags separately:

train(X_train_tokens:list[list[str]], y_train_tags:list[list[str]])

Or as token and tag pairs:

train_from_tagged_sents(tagged_sents:list[list[tuple[str,str]]])

After training, the model will be saved in the path you provided in the constructor.
Note: Training will override existing model with the same name.

Use Your Own Model

Just call the constructor with the model name:

pos_log=PosLogCRF(model_name="my_model")

Tokenization

Since PosLog was trained on a corpus we tokenized a specific way, we included the tokenizer PosLogTokenizer in this package.

We use three preprocessing steps before tokenization to adapt to log message specific characteristics:

1. We escape quotation marks with spaces to distinguish them from words with tailing apostrophes.

2. We extend NLTK's contraction list with 124 more cases where we split or replace contracted words.

3. We apply NLTK's word_tokenize which makes a few more replacements and returns a token list.

The following shows an example of the three steps of tokenization:

Example for the three steps of tokenization. 0 shows the input string and 3 the output list of tokenization.

0 (Input):  "Can't read 'block_x'."
1:          "Can't read 'block_x '."
2:          "Cannot read 'block_x '."
3 (Output): ["Can", "not", "read", "'", "block_x", "'", "."]

Dependencies

PosLog relies on

• nltk corpora: words, stopwords, wordnet and
• sklearn for the CRF classifier sklearn-crfsuite.