README.md

---
license: cc-by-nc-sa-4.0
language:
- en
- tr
tags:
- code-mixing
- turkish
- english
- LID
- NER
- code-switching
size_categories:
- 10K<n<100K
configs:
  - config_name: corpus
    data_files: TurEngMix_Corpus.csv
  - config_name: benchmark
    data_files: TurEngMix_Annotated_Benchmark.csv
  - config_name: benchmark_posts
    data_files: TurEngMix_Benchmark_Labelled_Posts.csv
---
# TurEngMix: A Text Corpus and Benchmark for Turkish-English Code-Mixed Language Identification and Named Entity Recognition

**Paper:** [link](https://arxiv.org/abs/2609.06963) · **Code:** [link](https://github.com/kemnguyenle/TurEngMix/tree/main) · **Dataset:** (this page)

## Abstract

Natural language processing systems underperform on code-mixed text, particularly for low-resource language pairs. Turkish-English poses a further challenge: it lets English stems combine with Turkish suffixes to form single mixed-language tokens. We introduce TurEngMix, a corpus of 5.5K noisy, naturally occurring social media posts (486,974 tokens) rich in Turkish-English code-mixing. From this corpus, we construct a new Turkish-English benchmark for code-mixed language identification (LID) and named entity recognition (NER), comprising 15K expert-annotated tokens. Evaluating both decoder LLM and fine-tuned encoder baselines, we find that monolingual Turkish and English tokens are labeled reliably, but all models have high error rates on mixed-language tokens for both LID and NER. For morphologically integrated tokens, NER error rates were 5.2× and 6.3× higher for GPT-4o and Qwen, respectively. This highlights how morphological integration remains a challenge.
We release the corpus, annotations, and code to support future computational and sociolinguistic research on Turkish-English code-mixing.

### Dataset Description

This release contains the TurEngMix corpus and the TurEngMix annotated benchmark with language ID and named entity labels for each word token.
- ~5500 full text posts
- ~15K annotated tokens
- Source: Turkish-English code-mixed social media text

## Dataset Structure

This release contains two related resources derived from the same collection pipeline:

### 1. TurEngMix Corpus
The full, unannotated corpus of naturally occurring Turkish-English code-mixed posts.

| | |
|---|---|
| File | TurEngMix_Corpus.csv |
| Posts | 5,549 |
| Tokens | 486,974 |
| Post length | 3–500 words (mean 87.75, median 57) |
| Format | One row per post: `topic`, `entry`, `word_count` |

This is the source pool the benchmark below was sampled from. It is not token-annotated and is intended for pretraining, further sampling, or corpus-level sociolinguistic study.

### 2. TurEngMix Annotated Benchmark
250 posts from the corpus above, tokenized and expert-annotated at the token level for language identification (LID) and named entity recognition (NER).

| | |
|---|---|
| File | TurEngMix_Annotated_Benchmark.csv |
| Posts | 250 |
| Sentences | 321 |
| Tokens | 15,012 |
| Format | One row per word token: 'doc_id', 'sent_id' 'tok_id', 'token', 'lid', 'integrated', 'ner' |

**Columns:**

| Column | Description |
|---|---|
| `doc_id` | Post identifier (e.g. `post_001`) |
| `sent_id` | Sentence index within the post |
| `tok_id` | Token index within the sentence (0-indexed, sequential) |
| `token` | The token text |
| `lid` | Language ID label (see schema below) |
| `integrated` | `MIXED` if the token is an English-origin stem with Turkish morphological suffixes attached (e.g. *influencer* + *-lar* + *-ımız*); blank otherwise |
| `ner` | Named entity label in BIO format (see schema below) |

**LID label schema** (adapted from Solorio et al. 2014):

| Label | Meaning |
|---|---|
| `TR` | Turkish |
| `EN` | English |
| `MIXED` | Token contains both an English-origin stem and Turkish morphology, and is not a named entity |
| `NE` | Named entity (any language of origin; overrides `TR`/`EN`/`MIXED`) |
| `AMBIGUOUS` | Could plausibly belong to either language given context |
| `OTHER` | Any other language |

**NER label schema** (BIO tagging, CALCS 2018 guidelines):
`O` (not an entity) plus B-/I- tags for: `PER` (person), `ORG` (organization), `LOC` (location), `GROUP` (sports teams, bands), `PROD` (product), `TITLE` (creative works), `EVENT`, `TIME`, `OTHER`.

### 3. TurEngMix Benchmark Posts
The 250 posts from the benchmark annotated for type of code mixing present in each post.

| | |
|---|---|
| File | TurEngMix_Benchmark_Labelled_Posts.csv |
| Posts | 250 |
| Format | One row per post: 'post_id',	'post_text', 'mixed_language_token','embedded_english_phrase', 'isolated_english_token' |

**Columns**

|Column | Description |
|---|---|
| `borrowed_suffix` | True if post contains at least one morphologically integrated token |
| `embedded_english_phrase` | True if post contains ≥2 consecutive English tokens |
| `isolated_english_token` | True if post contains an English token with no adjacent English tokens |

### Loading the data

```python
import pandas as pd

corpus = pd.read_csv("TurEngMix_Corpus.csv")
benchmark = pd.read_csv("TurEngMix_Annotated_Benchmark.csv")
benchmark_posts = pd.read_csv("TTurEngMix_Benchmark_Labelled_Posts.csv")

# Reconstruct a single post's tokens in order
post = benchmark[benchmark["doc_id"] == "post_001"].sort_values(["sent_id", "tok_id"])
```

---

## Intended Use

This dataset is released strictly for:

- Non-commercial research
- Academic use
- Model evaluation and benchmarking purposes

## Prohibited Uses

- Commercial use of any kind
- Redistribution or re-hosting of the dataset
- Training models for commercial deployment
- Attempting to identify individuals in the dataset

## Ethical Considerations

- Dataset may reflect biases present in social media
- No PII was deliberately collected or annotated
- Intended for research use only

## Citation

**BibTeX:**

<pre>
  @misc{dogan2026turengmixtextcorpusbenchmark,
      title={TurEngMix: A Text Corpus and Benchmark for Turkish-English Code-Mixed Language Identification and Named Entity Recognition}, 
      author={Ilayda Dogan and Phuong-Anh Nguyen-Le and Julia Mendelsohn},
      year={2026},
      eprint={2609.06963},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2609.06963}, 
}
</pre>

## Contact

For questions or access issues:

- idogan@umd.edu
- nlpa@umd.edu