# LFTK: Handcrafted Features in Computational Linguistics

arXiv: https://arxiv.org/abs/2305.15878 · HTML: https://mlsanity.com/paper/2305.15878/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 68 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 11 of 11)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [Trends, Limitations and Open Challenges in Automatic Readability Assessment Research](https://mlsanity.com/paper/2105.00973/) (2021)
- [MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification](https://mlsanity.com/paper/2109.06416/) (2021)
- [A Baseline Readability Model for Cebuano](https://mlsanity.com/paper/2203.17225/) (2022)
- [Automatic Readability Assessment of German Sentences with Transformer Ensembles](https://mlsanity.com/paper/2209.04299/) (2022)
- [LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea](https://mlsanity.com/paper/2010.13374/) (2020)
- [Traditional Readability Formulas Compared for English](https://mlsanity.com/paper/2301.02975/) (2023)
- [Auto-Select Reading Passages in English Assessment Tests?](https://mlsanity.com/paper/2205.06961/) (2022)

## Built on by (most cited first, 19 of 19)

- [Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach](https://mlsanity.com/paper/2310.06680/) (2023)
- [Analysing Zero-Shot Readability-Controlled Sentence Simplification](https://mlsanity.com/paper/2409.20246/) (2024)
- [RuleR: Improving LLM Controllability by Rule-based Data Recycling](https://mlsanity.com/paper/2406.15938/) (2024)
- [Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation](https://mlsanity.com/paper/2402.12593/) (2024)
- [Linguistic Blind Spots of Large Language Models](https://mlsanity.com/paper/2503.19260/) (2025)
- [Curriculum Learning for LLM Pretraining: An Analysis of Learning Dynamics](https://mlsanity.com/paper/2601.21698/) (2026)
- [EqualizeIR: Mitigating Linguistic Biases in Retrieval Models](https://mlsanity.com/paper/2504.07115/) (2025)
- [Multi-Objective Linguistic Control of Large Language Models](https://mlsanity.com/paper/2406.16229/) (2024)
- [A stylometric analysis of speaker attribution from speech transcripts](https://mlsanity.com/paper/2512.13667/) (2025)
- [Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability](https://mlsanity.com/paper/2310.00355/) (2023)
- [ParsiPy: NLP Toolkit for Historical Persian Texts in Python](https://mlsanity.com/paper/2503.17810/) (2025)
- [Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning](https://mlsanity.com/paper/2311.15728/) (2023)
- [EASSE-DE: Easier Automatic Sentence Simplification Evaluation for German](https://mlsanity.com/paper/2404.03563/) (2024)
- [LingGen: Scalable Multi-Attribute Linguistic Control via Power-Law Masking](https://mlsanity.com/paper/2410.24201/) (2024)
- [IntrEx: A Dataset for Modeling Engagement in Educational Conversations](https://mlsanity.com/paper/2509.06652/) (2025)
- [Controlling Language Difficulty in Dialogues with Linguistic Features](https://mlsanity.com/paper/2509.14545/) (2025)
- [Linguistic Blind Spots in Clinical Decision Extraction](https://mlsanity.com/paper/2602.03942/) (2026)
- [Adversarial Creation and Detection of AI-Generated Social Bot Content](https://mlsanity.com/paper/2606.07219/) (2026)
- [Analyzing Traditional and Neural Approaches to Multilingual Readability Assessment](https://mlsanity.com/paper/2609.10792/) (2026)

## Abstract (arXiv)

Past research has identified a rich set of handcrafted linguistic features that can potentially assist various tasks. However, their extensive number makes it difficult to effectively select and utilize existing handcrafted features. Coupled with the problem of inconsistent implementation across research works, there has been no categorization scheme or generally-accepted feature names. This creates unwanted confusion. Also, most existing handcrafted feature extraction libraries are not open-source or not actively maintained. As a result, a researcher often has to build such an extraction system from the ground up. We collect and categorize more than 220 popular handcrafted features grounded on past literature. Then, we conduct a correlation analysis study on several task-specific datasets and report the potential use cases of each feature. Lastly, we devise a multilingual handcrafted linguistic feature extraction system in a systematically expandable manner. We open-source our system for public access to a rich set of pre-implemented handcrafted features. Our system is coined LFTK and is the largest of its kind. Find it at github.com/brucewlee/lftk.