seqlabel 0.2.4

Rule-based Text Labeling Framework Aiming at Flexibility

seqlabel: Flexible Rule-based Text Labeling

seqlabel is a rule-based text labeling framework aiming at flexibility.

Installation

To install seqlabel:

pip install seqlabel

Requirements

• Python 3.8+

Usage

For a normal text

First, import some classes.

from seqlabel import Text
from seqlabel.matchers import DictionaryMatcher
from seqlabel.entity_filters import LongestMatchFilter, MaximizedMatchFilter
from seqlabel.serializers import IOB2Serializer

Initialize Text by giving it a text you want to label over.

text = Text("Tokyo is the capital of Japan.")

Prepare matcher matching supplied patterns. You can supply patterns via Hash Map mapping string sequences to the corresponding labels. You can define your own matcher by inheriting seqlabel.matchers.Matcher.

Then, apply matcher.match to text.

# Preparing Matcher
matcher = DictionaryMatcher()
# Adding patterns
matcher.add({"Tokyo": "LOC", "Japan": "LOC"})
# Matching
entities = matcher.match(text)

Filter unwanted entities. LongestMatchFilter removes overlapping entities and leaves longer entity. MaximizedMatchFilter removes overlapping entities and leaves as many entities as possible. You can define your own filter by inheriting seqlabel.entity_filters.EntityFilter.

filter_a = LongestMatchFilter()
filtered_entities_a = filter_a(entities)

filter_b = MaximizedMatchFilter()
filtered_entities_b = filter_b(entities)

Convert entities to IOB2 format after matching and filtering. Check seqlabel.serializers out if you want to use other formats.

serializer = IOB2Serializer()
serializer.save(text, filtered_entities_a)

For a tokenized text

If you want to process a tokenized text, you need to use TokenizedText instead of Text. You could import it as follows:

from seqlabel import TokenizedText

Initialize TokenizedText by giving it tokens and space_after you want to label over. tokens is a list of strings and space_after is a list of boolean indicating whether each token has a subsequent space.

tokenized_text = TokenizedText(
  ["Tokyo", "is", "the", "captial", "of", "Japan", "."],
  [True, True, True, True, True, False, False]
)

You can use matcher, filter, and serializer just like a normal text, as shown above.

# Mathcing
entities = matcher.match(tokenized_text)
# Filtering
filtered_entities = filter_a(entities)
# Serializing
serializer.save(tokenized_text, filtered_entities)