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README.md

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+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- text-classification
+task_ids:
+- binary-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)
+
+```python
+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:
+
+```python
+# 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:
+
+```python
+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/](https://aclanthology.org/N18-1019/)
+
+The words are drawn from news articles and annotated by 20 non-native English speakers who indicated which words they found difficult to understand.
+
+### 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](https://www.seas.upenn.edu/~cis5300/) at the University of Pennsylvania. Students build progressively more sophisticated classifiers:
+
+1. **Baselines**: Word length and word frequency thresholds
+2. **Machine learning**: Naive Bayes and Logistic Regression with engineered features
+3. **Custom models**: Additional features with error analysis
+4. **Domain generalization**: Evaluate on biomedical and news text
+
+## Citation
+
+```bibtex
+@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",
+}
+```

biomedical_test.csv

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+word,complexity_score,label
+role,0.0000,0
+role,0.2031,0
+role,0.2059,0
+role,0.2333,0
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+size,0.1250,0
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+top,0.1111,0
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+animal,0.1964,0
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news_test.csv

@@ -0,0 +1,1814 @@
+word,complexity_score,label
+teenage,0.0000,0
+londonderry,0.0500,1
+girl,0.0000,0
+shot,0.0000,0
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