Datasets:

CCB
/

cis5300-text-classification

language:
  - en
license: cc-by-4.0
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
pretty_name: Complex Word Identification
size_categories:
  - 1K<n<10K
tags:
  - text-simplification
  - lexical-complexity
  - cwi
  - nlp-course
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: validation
        path: validation.csv
      - split: test
        path: test.csv
  - config_name: biomedical
    data_files:
      - split: test
        path: biomedical_test.csv
  - config_name: news
    data_files:
      - split: test
        path: news_test.csv
dataset_info:
  - config_name: default
    features:
      - name: word
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': simple
              '1': complex
      - name: annotators
        dtype: int64
      - name: sentence
        dtype: string
      - name: sentence_index
        dtype: int64
    splits:
      - name: train
        num_examples: 4000
      - name: validation
        num_examples: 1000
      - name: test
        num_examples: 922
  - config_name: biomedical
    features:
      - name: word
        dtype: string
      - name: complexity_score
        dtype: float64
      - name: label
        dtype:
          class_label:
            names:
              '0': simple
              '1': complex
    splits:
      - name: test
        num_examples: 289
  - config_name: news
    features:
      - name: word
        dtype: string
      - name: complexity_score
        dtype: float64
      - name: label
        dtype:
          class_label:
            names:
              '0': simple
              '1': complex
    splits:
      - name: test
        num_examples: 1813

Complex Word Identification (CIS 5300)

Dataset Description

This dataset supports the Complex Word Identification (CWI) task: given a word in context, predict whether it is complex (likely to be difficult for non-native speakers, children, or people with reading disabilities) or simple.

CWI is the first step in lexical simplification — the task of rewriting text to make it more accessible. Before you can simplify a word, you need to identify which words need simplification.

Task

Input: A word and the sentence it appears in
Output: Binary label — 0 (simple) or 1 (complex)
Example: In the sentence "The beleaguered director resigned Wednesday", the word beleaguered is complex (label=1) while director is simple (label=0).

Dataset Structure

Default Configuration (Main Task)

from datasets import load_dataset

dataset = load_dataset("CCB/cis5300-text-classification")

# Splits: train (4,000), validation (1,000), test (922)
print(dataset["train"][0])
# {'word': 'string', 'label': 0, 'annotators': 0, 'sentence': 'WASHINGTON -- The beleaguered...', 'sentence_index': 27}

Split	Examples	Labels
train	4,000	Yes
validation	1,000	Yes
test	922	No (held out for evaluation)

Fields:

Field	Type	Description
`word`	string	The target word to classify
`label`	ClassLabel (simple/complex)	0 = simple, 1 = complex
`annotators`	int	Number of annotators who labeled the word as complex
`sentence`	string	The full sentence containing the target word
`sentence_index`	int	Token position of the target word in the sentence

Domain Generalization Configs

Two additional test sets from different domains for evaluating how well models generalize:

# Biomedical domain (PubMed abstracts)
bio = load_dataset("CCB/cis5300-text-classification", "biomedical")

# News domain (from CWI 2018 shared task)
news = load_dataset("CCB/cis5300-text-classification", "news")

Config	Domain	Examples	Source
`biomedical`	PubMed abstracts	289	CompLex (Shardlow et al., 2020)
`news`	News articles	1,813	CWI 2018 Shared Task (Yimam et al., 2018)

Supplementary Data

The file ngram_counts.txt.gz contains word frequency counts from Google Books N-grams (~8.7M entries). This is useful as a feature for classification but is not a structured dataset. Download it separately:

from huggingface_hub import hf_hub_download
path = hf_hub_download("CCB/cis5300-text-classification", "ngram_counts.txt.gz", repo_type="dataset")

import gzip
ngram_counts = {}
with gzip.open(path, 'rt', encoding='utf-8') as f:
    for line in f:
        parts = line.strip().split('\t')
        if len(parts) == 2:
            ngram_counts[parts[0]] = int(parts[1])

Source

The core dataset was introduced in:

Simplification Using Paraphrases and Context-Based Lexical Substitution Reno Kriz, Eleni Miltsakaki, Marianna Apidianaki, Chris Callison-Burch Proceedings of NAACL-HLT 2018, pages 207–217 https://aclanthology.org/N18-1019/

The words are drawn from news articles and annotated by both native and non-native English speakers who indicated which words could be difficult for non-native speakers, children, or people with reading disabilities.

Annotation scheme and binarization

Each word was independently labeled by approximately 10 annotators. The annotators column records how many annotators marked the word as complex. Binary labels were derived using a threshold:

Simple (0): No annotator marked the word as complex (annotators = 0)
Complex (1): Three or more annotators marked it as complex (annotators >= 3)
Excluded: Words in the ambiguous zone (1–2 annotators) were removed from the dataset

This means the dataset contains only clear cases — words that are unambiguously simple or where a meaningful fraction of annotators agreed on complexity.

Domain generalization sources

Biomedical: From the CompLex dataset (Shardlow et al., 2020) — words from biomedical abstracts with continuous complexity scores binarized at threshold 0.5.
News: From the CWI 2018 Shared Task (Yimam et al., 2018) — words from news articles annotated for complexity.

Intended Use

This dataset is used for Homework 1 in CIS 5300: Natural Language Processing at the University of Pennsylvania. Students build progressively more sophisticated classifiers:

Baselines: Word length and word frequency thresholds
Machine learning: Naive Bayes and Logistic Regression with engineered features
Custom models: Additional features with error analysis
Domain generalization: Evaluate on biomedical and news text

Citation

@inproceedings{kriz-etal-2018-simplification,
    title = "Simplification Using Paraphrases and Context-Based Lexical Substitution",
    author = "Kriz, Reno and Miltsakaki, Eleni and Apidianaki, Marianna and Callison-Burch, Chris",
    booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-1019",
    doi = "10.18653/v1/N18-1019",
    pages = "207--217",
}