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Example of Labeling Functions

A collection of labeling functions for row labeling and span labeling.

Labeling functions apply heuristics and rules to predict labels for unlabeled data. They can be based on domain knowledge or outputs from other models. Labeling functions are written in Python and can include:

  • Keyword searches with regular expressions: For example, detecting severity scores in numeric or Roman numeral formats.

  • Text preprocessing and NLP techniques: Using libraries such as NLTK, Spacy, or TextBlob for tasks like POS tagging, sentiment analysis, named entity recognition (NER), dependency parsing, and similarity measures.

Row labeling

OpenAI ChatGPT sports
import re
from stegosaurus.annotator import target_label

# This is your constant labels dictionary, SHOULD NOT BE EDITED
LABELS = {
  'business' : 0,
  'science' : 1,
  'sports' : 2,
  'world' : 3
}

# Labeling function definition (Start editing here!)
import openai
openai.api_key = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
# Assign target label based on LABELS dictionary
@target_label(label=LABELS['sports'])
def label_function(sample):
  text = list(sample.values())[0]

  # Implement your logic here
  completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[
      {"role": "system", "content": "You are an Expert Data Labeler in classifying text as sports by detecting match score"},
      {
        "role": "user", 
        "content": """Sentence: Treat Huey, the two-time All-Met Player of the year, suffered a 6-2, 6-0 loss to 29-year-old journeyman Mashiska Washington, the younger brother of former pro and 1996 Wimbledon finalist Malivai.

          What is the match score from sentence above? Is the text above classified as sports?"""},
      {"role": "assistant", "content": "[6-2,6-0];yes"},
      {
        "role": "user", 
        "content": """Sentence: Pay for the Washington area's top executives rose significantly last year, reversing the downward trend that set in with the recession in 2001.

          What is the match score from sentence above? Is the text above classified as sports?"""},
      {"role": "assistant", "content": '[];no'},
      {
        "role": "user", 
        "content": """Sentence: {}
          
          What is the match score from sentence above? Is the text above classified as sports?""".format(text)}
    ]
  )
  match_text = completion['choices'][0]['message']['content']

	#match_text is string
	#process match_text to see if the result sufficient the logic of labeling function or not
  detected_score, is_sport = match_text.split(';')
  if detected_score!='[]' and is_sport=='yes':
    return True
  return False
Keyword search world
import re
from stegosaurus.annotator import target_label

# This is your constant labels dictionary, SHOULD NOT BE EDITED
LABELS = {
  'business' : 0, 
  'science' : 1, 
  'sports' : 2, 
  'world' : 3
}

# Labeling function definition (Start editing here!)
# Assign target label based on LABELS dictionary
@target_label(label=LABELS['world'])
def label_function(sample):
  
  text = list(sample.values())[0]

  # Implement your logic here
  TARGET_KEYWORDS = ['confrontation', 'violent', 'harrassed','fight', 'vehicle', 'government', 'employment', 'military', 'war']
  for keyword in TARGET_KEYWORDS:
    keyword = keyword.replace("\\\\", '')
    if re.search(keyword, text, re.IGNORECASE):
      return True
  return False
Regex rule sports
import re
from stegosaurus.annotator import target_label

# This is your constant labels dictionary, SHOULD NOT BE EDITED
LABELS = {
  'business' : 0, 
  'science' : 1, 
  'sports' : 2, 
  'world' : 3
}

# Labeling function definition (Start editing here!)
# Assign target label based on LABELS dictionary
@target_label(label=LABELS['sports'])
def label_function(sample):
  
  text = list(sample.values())[0]

  # Implement your logic here
  score = re.compile(r"\\b(0|[1-9]\\d*)-(0|[1-9]\\d*)\\b")
  PATTERNS = [score]
  for pattern in PATTERNS:
    if re.search(pattern, text):
      return True
  return False
spaCy world
import re
from stegosaurus.annotator import target_label

# This is your constant labels dictionary, SHOULD NOT BE EDITED
LABELS = {
  'business' : 0, 
  'science' : 1, 
  'sports' : 2, 
  'world' : 3
}

# Labeling function definition (Start editing here!)
import spacy
nlp = spacy.load("en_core_web_sm")
NER_LABELS = ["NORP"]

# Assign target label based on LABELS dictionary
@target_label(label=LABELS['world'])
def label_function(sample):
  
  text = list(sample.values())[0]

  # Implement your logic here
  spacy_pred = nlp(text)
  TARGET_KEYWORDS = []
  for token in spacy_pred.ents:
    token_label = token.label_
    if token_label in NER_LABELS:
      TARGET_KEYWORDS.append(str(token))

  for keyword in TARGET_KEYWORDS:
    keyword = keyword.replace("\\\\", '')
    if re.search(keyword, text, re.IGNORECASE):
      return True
  return False
TextBlob (for sentiment analysis) positive

Span labeling

OpenAI ChatGPT event
Regex rule police name
Keyword prefix suspect name
Keyword match slang label
spaCy time

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