Tag: artificial-intelligence

I-Language vs. E-Language: What Do They Mean in Computational Linguistics?

In the summer of 2025, I started working on a computational linguistics research project using Twitch data under the guidance of Dr. Sidney Wong, a Computational Sociolinguist. As someone who is still pretty new to this field, I was mainly focused on learning how to conduct literature reviews, help narrow down research topics, clean data, build models, and extract insights.

One day, Dr. Wong suggested I look into the concept of I-language vs. E-language from theoretical linguistics. At first, I wasn’t sure why this mattered. I thought, Isn’t language just… language?

But as I read more, I realized that understanding this distinction changes how we think about language data and what we’re actually modeling when we work with NLP.

In this post, I want to share what I’ve learned about I-language and E-language, and why this distinction is important for computational linguistics research.

What Is I-Language?

I-language stands for “internal language.” This idea was proposed by Noam Chomsky, who argued that language is fundamentally a mental system. I-language refers to the internal, cognitive grammar that allows us to generate and understand sentences. It is about:

  • The unconscious rules and structures stored in our minds
  • Our innate capacity for language
  • The mental system that explains why we can produce and interpret sentences we’ve never heard before

For example, if I say, “The cat sat on the mat,” I-language is the system in my brain that knows the sentence is grammatically correct and what it means, even though I may never have said that exact sentence before.

I-language focuses on competence (what we know about our language) rather than performance (how we actually use it in real life).

What Is E-Language?

E-language stands for “external language.” This is the language we actually hear and see in the world, such as:

  • Conversations between Twitch streamers and their viewers
  • Tweets, Reddit posts, books, and articles
  • Any linguistic data that exists outside the mind

E-language is about observable language use. It includes everything from polished academic writing to messy chat messages filled with abbreviations, typos, and slang.

Instead of asking, “What knowledge do speakers have about their language?”, E-language focuses on, “What do speakers actually produce in practice?”

Why Does This Matter for Computational Linguistics?

When it comes to computational linguistics and NLP, this distinction affects:

1. What We Model

  • I-language-focused research tries to model the underlying grammatical rules and mental representations. For example, building a parser that captures syntax structures based on linguistic theory.
  • E-language-focused research uses real-world data to build models that predict or generate language based on patterns, regardless of theoretical grammar. For example, training a neural network on millions of Twitch comments to generate chat responses.

2. Research Goals

If your goal is to understand how humans process and represent language cognitively, you’re leaning towards I-language research. This includes computational psycholinguistics, cognitive modeling, and formal grammar induction.

If your goal is to build practical NLP systems for tasks like translation, summarization, or sentiment analysis, you’re focusing on E-language. These projects care about performance and usefulness, even if the model doesn’t match linguistic theory.

3. How Models Are Evaluated

I-language models are evaluated based on how well they align with linguistic theory or native speaker intuitions about grammaticality.

E-language models are evaluated using performance metrics, such as accuracy, BLEU scores, or perplexity, based on how well they handle real-world data.

My Thoughts as a Beginner

When Dr. Wong first told me about this distinction, I thought it was purely theoretical. But now, while working with Twitch data, I see the importance of both views.

For example:

  • If I want to study how syntax structures vary in Twitch chats, I need to think in terms of I-language to analyze grammar.
  • If I want to build an NLP model that generates Twitch-style messages, I need to focus on E-language to capture real-world usage patterns.

Neither approach is better than the other. They just answer different types of questions. I-language is about why language works the way it does, while E-language is about how language is actually used in the world.

Final Thoughts

Understanding I-language vs. E-language helps me remember that language isn’t just data for machine learning models. It’s a human system with deep cognitive and social layers. Computational linguistics becomes much more meaningful when we consider both perspectives: What does the data tell us? and What does it reveal about how humans think and communicate?

If you’re also just starting out in this field, I hope this post helps you see why these theoretical concepts matter for practical NLP and AI work. Let me know if you want a follow-up post about other foundational linguistics ideas for computational research.

— Andrew

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What Is Computational Linguistics (and How Is It Different from NLP)?

When I first got interested in this field, I kept seeing the terms computational linguistics and natural language processing (NLP) used almost interchangeably. At first, I thought they were the same thing. By delving deeper through reading papers, taking courses, and conducting research, I realized that although they overlap significantly, they are not entirely identical.

So in this post, I want to explain the difference (and connection) between computational linguistics and NLP from the perspective of a high school student who’s just getting started, but really interested in understanding both the language and the tech behind today’s AI systems.

So, what is computational linguistics?

Computational linguistics is the science of using computers to understand and model human language. It’s rooted in linguistics, the study of how language works, and applies computational methods to test linguistic theories, analyze language structure, or build tools like parsers and grammar analyzers.

It’s a field that sits at the intersection of computer science and linguistics. Think syntax trees, morphology, phonology, semantics, and using code to work with all of those.

For example, in computational linguistics, you might:

  • Use code to analyze sentence structure in different languages
  • Create models that explain how children learn grammar rules
  • Explore how prosody (intonation and stress) changes meaning in speech
  • Study how regional dialects appear in online chat platforms like Twitch

In other words, computational linguistics is often about understanding language (how it’s structured, how it varies, and how we can model it with computers).

Then what is NLP?

Natural language processing (NLP) is a subfield of AI and computer science that focuses on building systems that can process and generate human language. It’s more application-focused. If you’ve used tools like ChatGPT, Google Translate, Siri, or even grammar checkers, you’ve seen NLP in action.

While computational linguistics asks, “How does language work, and how can we model it?”, NLP tends to ask, “How can we build systems that understand or generate language usefully?”

Examples of NLP tasks:

  • Sentiment analysis (e.g., labeling text as positive, negative, or neutral)
  • Machine translation
  • Named entity recognition (e.g., tagging names, places, dates)
  • Text summarization or question answering

In many cases, NLP researchers care more about whether a system works than whether it matches a formal linguistic theory. That doesn’t mean theory doesn’t matter, but the focus is more on performance and results.

So, what’s the difference?

The line between the two fields can get blurry (and many people work in both), but here’s how I think of it:

Computational LinguisticsNLP
Rooted in linguisticsRooted in computer science and AI
Focused on explaining and modeling languageFocused on building tools and systems
Often theoretical or data-driven linguisticsOften engineering-focused and performance-driven
Examples: parsing syntax, studying morphologyExamples: sentiment analysis, machine translation

Think of computational linguistics as the science of language and NLP as the engineering side of language technology.

Why this matters to me

As someone who’s really interested in computational linguistics, I find myself drawn to the linguistic side of things, like how language varies, how meaning is structured, and how AI models sometimes get things subtly wrong because they don’t “understand” language the way humans do.

At the same time, I still explore NLP, especially when working on applied projects like sentiment analysis or topic modeling. I think having a strong foundation in linguistics makes me a better NLP researcher (or student), because I’m more aware of the complexity and nuance of language.

Final thoughts

If you’re just getting started, you don’t have to pick one or the other. Read papers from both fields. Try projects that help you learn both theory and application. Over time, you’ll probably find yourself leaning more toward one, but having experience in both will only help.

I’m still learning, and I’m excited to keep going deeper into both sides. If you’re interested too, let me know! I’m always up for sharing reading lists, courses, or just thoughts on cool research.

— Andrew

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My Thoughts on “The Path to Medical Superintelligence”

Recently, I read an article published on Microsoft AI’s blog titled “The Path to Medical Superintelligence”. As a high school student interested in AI, computational linguistics, and the broader impacts of technology, I found this piece both exciting and a little overwhelming.

What Is Medical Superintelligence?

The blog talks about how Microsoft AI is working to build models with superhuman medical reasoning abilities. In simple terms, the idea is to create an AI that doesn’t just memorize medical facts but can analyze, reason, and make decisions at a level that matches or even surpasses expert doctors.

One detail that really stood out to me was how their new AI models also consider the cost of healthcare decisions. The article explained that while health costs vary widely depending on country and system, their team developed a method to consistently measure trade-offs between diagnostic accuracy and resource use. In other words, the AI doesn’t just focus on getting the diagnosis right, but also weighs how expensive or resource-heavy its suggested tests and treatments would be.

They explained that their current models already show impressive performance on medical benchmarks, such as USMLE-style medical exams, and that future models could go beyond question answering to support real clinical decision-making in a way that is both effective and efficient.

What Excites Me About This?

One thing that stood out to me was the potential impact on global health equity. The article mentioned that billions of people lack reliable access to doctors or medical specialists. AI models with advanced medical reasoning could help provide high-quality medical advice anywhere, bridging the gap for underserved communities.

It’s also amazing to think about how AI could support doctors by:

  • Reducing their cognitive load
  • Cross-referencing massive amounts of research
  • Helping with diagnosis and treatment planning

For someone like me who is fascinated by AI’s applications in society, this feels like a real-world example of AI doing good.

What Concerns Me?

At the same time, the blog post emphasized that AI is meant to complement doctors and health professionals, not replace them. I completely agree with this perspective. Medical decisions aren’t just about making the correct diagnosis. Doctors also need to navigate ambiguity, understand patient emotions and values, and build trust with patients and their families in ways AI isn’t designed to do.

Still, even if AI is only used as a tool to support clinicians, there are important concerns:

  • AI could give wrong or biased recommendations if the training data is flawed
  • It might suggest treatments without understanding a patient’s personal situation or cultural background
  • There is a risk of creating new inequalities if only wealthier hospitals or countries can afford the best AI models

Another thought I had was about how roles will evolve. The article mentioned that AI could help doctors automate routine tasks, identify diseases earlier, personalize treatment plans, and even help prevent diseases altogether. This sounds amazing, but it also means future doctors will need to learn how to work with AI systems effectively, interpret their recommendations, and still make the final decisions with empathy and ethical reasoning.

Connections to My Current Interests

While this blog post was about medical AI, it reminded me of my own interests in computational linguistics and language models. Underneath these medical models are the same AI principles I study:

  • Training on large datasets
  • Fine-tuning models for specific tasks
  • Evaluating performance carefully and ethically

It also shows how domain-specific knowledge (like medicine) combined with AI skills can create powerful tools that can literally save lives. That motivates me to keep building my foundation in both language technologies and other fields, so I can be part of these interdisciplinary innovations in the future.

Final Thoughts

Overall, reading this blog post made me feel hopeful about the potential of AI in medicine, but also reminded me of the responsibility AI developers carry. Creating a medical superintelligence isn’t just about reaching a technological milestone. It’s about improving people’s lives safely, ethically, and equitably.

If you’re interested in AI for social good, I highly recommend reading the full article here. Let me know if you want me to write a future post about other applications of AI that I’ve been exploring this summer.

— Andrew

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SCiL vs. ACL: What’s the Difference? (A Beginner’s Take from a High School Student)

As a high school student just starting to explore computational linguistics, I remember being confused by two organizations: SCiL (Society for Computation in Linguistics) and ACL (Association for Computational Linguistics). They both focus on language and computers, so at first, I assumed they were basically the same thing.

It wasn’t until recently that I realized they are actually two different academic communities. Each has its own focus, audience, and style of research. I’ve had the chance to engage with both, which helped me understand how they are connected and how they differ.

Earlier this year, I had the opportunity to co-author a paper that was accepted to a NAACL 2025 workshop (May 3–4). NAACL stands for the North American Chapter of the Association for Computational Linguistics. It is a regional chapter that serves researchers in the United States, Canada, and Mexico. NAACL follows ACL’s mission and guidelines but focuses on more local events and contributions.

This summer, I will be participating in SCiL 2025 (July 18–19), where I hope to meet researchers and learn more about how computational models are used to study language structure and cognition. Getting involved with both events helped me better understand what makes SCiL and ACL unique, so I wanted to share what I’ve learned for other students who might also be starting out.

SCiL and ACL: Same Field, Different Focus

Both SCiL and ACL are academic communities interested in studying human language using computational methods. However, they focus on different kinds of questions and attract different types of researchers.

Here’s how I would explain the difference.

SCiL (Society for Computation in Linguistics)

SCiL is more focused on using computational tools to support linguistic theory and cognitive science. Researchers here are often interested in how language works at a deeper level, including areas like syntax, semantics, and phonology.

The community is smaller and includes people from different disciplines like linguistics, psychology, and cognitive science. You are likely to see topics such as:

  • Computational models of language processing
  • Formal grammars and linguistic structure
  • Psycholinguistics and cognitive modeling
  • Theoretical syntax and semantics

If you are interested in how humans produce and understand language, and how computers can help us model that process, SCiL might be a great place to start.

ACL (Association for Computational Linguistics)

ACL has a broader and more applied focus. It is known for its work in natural language processing (NLP), artificial intelligence, and machine learning. The research tends to focus on building tools and systems that can actually use human language in practical ways.

The community is much larger and includes researchers from both academia and major tech companies like Google, OpenAI, Meta, and Microsoft. You will see topics such as:

  • Language models like GPT, BERT, and LLaMA
  • Machine translation and text summarization
  • Speech recognition and sentiment analysis
  • NLP benchmarks and evaluation methods

If you want to build or study real-world AI systems that use language, ACL is the place where a lot of that cutting-edge research is happening.

Which One Should You Explore First?

It really depends on what excites you most.

If you are curious about how language works in the brain or how to use computational tools to test theories of language, SCiL is a great choice. It is more theory-driven and focused on cognitive and linguistic insights.

If you are more interested in building AI systems, analyzing large datasets, or applying machine learning to text and speech, then ACL might be a better fit. It is more application-oriented and connected to the latest developments in NLP.

They both fall under the larger field of computational linguistics, but they come at it from different angles. SCiL is more linguistics-first, while ACL is more NLP-first.

Final Thoughts

I am still early in my journey, but understanding the difference between SCiL and ACL has already helped me navigate the field better. Each community asks different questions, uses different methods, and solves different problems, but both are helping to push the boundaries of how we understand and work with language.

I am looking forward to attending SCiL 2025 this summer, and I will definitely write about that experience afterward. In the meantime, I hope this post helps other students who are just starting out and wondering where to begin.

— Andrew

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Is It Legal to Train AI on Books? A High School Researcher’s Take on the Anthropic Ruling

As someone who’s been exploring computational linguistics and large language models (LLMs), I’ve always wondered: How legal is it, really, to train AI on books or copyrighted material? This question came up while I was learning about how LLMs are trained using massive datasets, including books, articles, and other written works. It turns out the legal side is just as complex as the technical side.

A major U.S. court case in June 2025 helped answer this question, at least for now. In this post, I’ll break down what happened and what it means for researchers, developers, and creators.

The Big Picture: Copyright, Fair Use, and AI

In the U.S., books and intellectual property (IP) are protected under copyright law. That means you can’t just use someone’s novel or article however you want, especially if it’s for a commercial product.

However, there’s something called fair use, which allows limited use of copyrighted material without permission. Whether something qualifies as fair use depends on four factors:

  1. The purpose of the use (such as commercial vs. educational)
  2. The nature of the original work
  3. The amount used
  4. The effect on the market value of the original

LLM developers often argue that training models is “transformative.” In other words, the model doesn’t copy the books word for word. Instead, it learns patterns from large collections of text and generates new responses based on those patterns.

Until recently, this argument hadn’t been fully tested in court.

What Just Happened: The Anthropic Case (June 24, 2025)

In a landmark decision, U.S. District Judge William Alsup ruled that AI company Anthropic did not violate copyright law when it trained its Claude language model on books. The case was brought by authors Andrea Bartz, Charles Graeber, and Kirk Wallace Johnson, who argued that Anthropic had used their work without permission.

  • Andrea Bartz: The Lost Night: A Novel
  • Charles Graeber: The Good Nurse: A True Story of Medicine, Madness, and Murder
  • Kirk Wallace Johnson: The Fisherman and the Dragon: Fear, Greed, and a Fight for Justice on the Gulf Coast

Judge Alsup ruled that Anthropic’s use of the books qualified as fair use. He called the training process “exceedingly transformative” and explained that the model did not attempt to reproduce the authors’ styles or specific wording. Instead, the model learned patterns and structures in order to generate new language, similar to how a human might read and learn from books before writing something original.

However, the court also found that Anthropic made a serious mistake. The company had copied and stored more than 7 million pirated books in a central data library. Judge Alsup ruled that this was not fair use and was a clear violation of copyright law. A trial is scheduled for December 2025 to determine possible penalties, which could be up to $150,000 per work.

Why This Case Matters

This is the first major U.S. court ruling on whether training generative AI on copyrighted works can qualify as fair use. The result was mixed. On one hand, the training process itself was ruled legal. On the other hand, obtaining the data illegally was not.

This means AI companies can argue that their training methods are transformative, but they still need to be careful about where their data comes from. Using pirated books, even if the outcome is transformative, still violates copyright law.

Other lawsuits are still ongoing. Companies like OpenAI, Meta, and Microsoft are also facing legal challenges from authors and publishers. These cases may be decided differently, depending on how courts interpret fair use.

My Thoughts as a Student Researcher

To be honest, I understand both sides. As someone who is really excited about the possibilities of LLMs and has worked on research projects involving language models, I think it’s important to be able to learn from large and diverse datasets.

At the same time, I respect the work of authors and creators. Writing a book takes a lot of effort, and it’s only fair that their rights are protected. If AI systems are going to benefit from their work, then maybe there should be a system that gives proper credit or compensation.

For student researchers like me, this case is a reminder to be careful and thoughtful about where our data comes from. It also raises big questions about what responsible AI development looks like, not just in terms of what is allowed by law, but also what is fair and ethical.

Wrapping It Up

The Anthropic ruling is a big step toward defining the legal boundaries for training AI on copyrighted material. It confirmed that training can be legal under fair use if it is transformative, but it also made clear that sourcing content from pirated platforms is still a violation of copyright law.

This case does not settle the global debate, but it does provide some clarity for researchers and developers in the U.S. Going forward, the challenge will be finding a balance between supporting innovation and respecting the rights of creators.

— Andrew

Update (September 5, 2025):

AI startup Anthropic will pay at least $1.5 billion to settle a copyright infringement lawsuit over its use of books downloaded from the Internet to train its Claude AI models. The federal case, filed last year in California by several authors, accused Anthropic of illegally scraping millions of works from ebook piracy sites. As part of the settlement, Anthropic has agreed to destroy datasets containing illegally accessed works. (Read the full report)

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Back from Hibernation — A Paper, a Robot, and a Lot of Tests

It’s been a while—almost three months since my last post. Definitely not my usual pace. I wanted to check in and share why the blog has been a bit quiet recently—and more importantly, what I’ve been working on behind the scenes.

First, April and May were a whirlwind: I had seven AP exams, school finals, and was deep in preparation for the VEX Robotics World Championship. Balancing school with intense robotics scrimmages and code debugging meant there were a lot of late nights and early mornings—and not much time to write.

But the biggest reason for the radio silence? I’ve been working on a research paper that got accepted to NAACL 2025.

Our NAACL 2025 Paper: “A Bag-of-Sounds Approach to Multimodal Hate Speech Detection”

Over the past few months, I’ve had the opportunity to co-author a paper with Dr. Sidney Wong, focusing on multimodal hate speech detection using audio data. The paper was accepted to the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages at NAACL 2025.

You can read the full paper here:
👉 A Bag-of-Sounds Approach to Multimodal Hate Speech Detection

2025.dravidianlangtech-1.94Download

What we did:
We explored a “bag-of-sounds” method, training our model on Mel spectrogram features extracted from spoken social media content in Dravidian languages—specifically Malayalam and Tamil. Unlike most hate speech systems that rely solely on text, we wanted to see how well speech-based signals alone could perform.

How it went:
The results were mixed. Our system didn’t perform great on the final test set—but on the training and dev sets, we saw promise. The takeaway? With enough balanced and labeled audio data, speech can absolutely play a role in multimodal hate speech detection systems. It’s a step toward understanding language in more realistic, cross-modal contexts.

More importantly, this project helped me dive into the intersection of language, sound, and AI—and reminded me just how much we still have to learn when it comes to processing speech from low-resource languages.

Thanks for sticking around even when the blog went quiet. I’ll be back soon with a post about my experience at the VEX Robotics World Championship—stay tuned!

— Andrew

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My First Solo Publication: A Case Study on Sentiment Analysis in Survey Data

I’m excited to share that my first solo-authored research paper has just been published in the National High School Journal of Science! 🎉

The paper is titled “A Case Study of Sentiment Analysis on Survey Data Using LLMs versus Dedicated Neural Networks”, and it explores a question I’ve been curious about for a while: how do large language models (like GPT-4o or LLaMA-3) compare to task-specific neural networks when it comes to analyzing open-ended survey responses?

If you’ve read some of my earlier posts—like my reflection on the DravidianLangTech shared task or my thoughts on Jonathan Dunn’s NLP book—you’ll know that sentiment analysis has become a recurring theme in my work. From experimenting with XLM-RoBERTa on Tamil and Tulu to digging into how NLP can support corpus linguistics, this paper feels like the natural next step in that exploration.

Why This Matters to Me

Survey responses are messy. They’re full of nuance, ambiguity, and context—and yet they’re also where we hear people’s honest voices. I’ve always thought it would be powerful if AI could help us make sense of that kind of data, especially in educational or public health settings where understanding sentiment could lead to real change.

In this paper, I compare how LLMs and dedicated models handle that challenge. I won’t go into the technical details here (the paper does that!), but one thing that stood out to me was how surprisingly effective LLMs are—even without task-specific fine-tuning.

That said, they come with trade-offs: higher computational cost, more complexity, and the constant need to assess bias and interpretability. There’s still a lot to unpack in this space.

Looking Ahead

This paper marks a milestone for me, not just academically but personally. It brings together things I’ve been learning in courses, competitions, side projects, and books—and puts them into conversation with each other. I’m incredibly grateful to the mentors and collaborators who supported me along the way.

If you’re interested in sentiment analysis, NLP for survey data, or just want to see what a high school research paper can look like in this space, I’d love for you to take a look:
🔗 Read the full paper here

Thanks again for following along this journey. Stay tuned!

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Shared Task at DravidianLangTech 2025

In 2025, I had the privilege of participating in the shared task on Sentiment Analysis in Tamil and Tulu as part of the DravidianLangTech@NAACL 2025 conference. The task was both challenging and enlightening, as it required applying machine learning techniques to multilingual data with varying sentiment nuances. This post highlights the work I did, the methodology I followed, and the results I achieved.

The Task at Hand

The goal of the task was to classify text into one of four sentiment categories: Positive, Negative, Mixed Feelings, and Unknown State. The datasets provided were in Tamil and Tulu, which made it a fascinating opportunity to work with underrepresented languages.

Methodology

I implemented a pipeline to preprocess the data, tokenize it, train a transformer-based model, and evaluate its performance. My choice of model was XLM-RoBERTa, a multilingual transformer capable of handling text from various languages effectively. Below is a concise breakdown of my approach:

  1. Data Loading and Inspection:
    • Used training, validation, and test datasets in .xlsx format.
    • Inspected the data for missing values and label distributions.
  2. Text Cleaning:
    • Created a custom function to clean text by removing unwanted characters, punctuation, and emojis.
    • Removed common stopwords to focus on meaningful content.
  3. Tokenization:
    • Tokenized the cleaned text using the pre-trained XLM-RoBERTa tokenizer with a maximum sequence length of 128.
  4. Model Setup:
    • Leveraged XLM-RoBERTaForSequenceClassification with 4 output labels.
    • Configured TrainingArguments to train for 3 epochs with evaluation at the end of each epoch.
  5. Evaluation:
    • Evaluated the model on the validation set, achieving a Validation Accuracy of 59.12%.
  6. Saved Model:
    • Saved the trained model and tokenizer for reuse.

Results

After training the model for three epochs, the validation accuracy was 59.12%. While there is room for improvement, this score demonstrates the model’s capability to handle complex sentiment nuances in low-resource languages like Tamil.

The Code

Below is an overview of the steps in the code:

  • Preprocessing: Cleaned and tokenized the text to prepare it for model input.
  • Model Training: Used Hugging Face’s Trainer API to simplify the training process.
  • Evaluation: Compared predictions against ground truth to compute accuracy.

To make this process more accessible, I’ve attached the complete code as a downloadable file. However, for a quick overview, here’s a snippet from the code that demonstrates how the text was tokenized:

# Tokenize text data using the XLM-RoBERTa tokenizer
def tokenize_text(data, tokenizer, max_length=128):
    return tokenizer(
        data,
        truncation=True,
        padding='max_length',
        max_length=max_length,
        return_tensors="pt"
    )

train_tokenized = tokenize_text(train['cleaned'].tolist(), tokenizer)
val_tokenized = tokenize_text(val['cleaned'].tolist(), tokenizer)

This function ensures the input text is prepared correctly for the transformer model.

Reflections

Participating in this shared task was a rewarding experience. It highlighted the complexities of working with low-resource languages and the potential of transformers in tackling these challenges. Although the accuracy could be improved with hyperparameter tuning and advanced preprocessing, the results are a promising step forward.

Download the Code

I’ve attached the full code used for this shared task. Feel free to download it and explore the implementation in detail.

XLM-RoBERTa Model And EvalDownload

If you’re interested in multilingual NLP or sentiment analysis, I’d love to hear your thoughts or suggestions on improving this approach! Leave a comment below or connect with me via the blog.

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Happy New Year 2025! Reflecting on a Year of Growth and Looking Ahead

As we welcome 2025, I want to take a moment to reflect on the past year and share some exciting plans for the future.

Highlights from 2024

  • Academic Pursuits: I delved deeper into Natural Language Processing (NLP), discovering Jonathan Dunn’s Natural Language Processing for Corpus Linguistics, which seamlessly integrates computational methods with traditional linguistic analysis.
  • AI and Creativity: Exploring the intersection of AI and human creativity, I read Garry Kasparov’s Deep Thinking, which delves into his experiences with AI in chess and offers insights into the evolving relationship between humans and technology.
  • Competitions and Courses: I actively participated in Kaggle competitions, enhancing my machine learning and data processing skills, which are crucial in the neural network and AI aspects of Computational Linguistics.
  • Community Engagement: I had the opportunity to compete in the 2024 VEX Robotics World Championship and reintroduced our school’s chess club to the competitive scene, marking our return since pre-COVID times.

Looking Forward to 2025

  • Expanding Knowledge: I plan to continue exploring advanced topics in NLP and AI, sharing insights and resources that I find valuable.
  • Engaging Content: Expect more in-depth discussions, tutorials, and reviews on the latest developments in computational linguistics and related fields.
  • Community Building: I aim to foster a community where enthusiasts can share knowledge, ask questions, and collaborate on projects.

Thank you for being a part of this journey. Your support and engagement inspire me to keep exploring and sharing. Here’s to a year filled with learning, growth, and innovation!

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A Book That Expanded My Perspective on NLP: Natural Language Processing for Corpus Linguistics by Jonathan Dunn

Book Link: https://doi.org/10.1017/9781009070447

As I dive deeper into the fascinating world of Natural Language Processing (NLP), I often come across resources that reshape my understanding of the field. One such recent discovery is Jonathan Dunn’s Natural Language Processing for Corpus Linguistics. This book, a part of the Elements in Corpus Linguistics series by Cambridge University Press, stands out for its seamless integration of computational methods with traditional linguistic analysis.

A Quick Overview

The book serves as a guide to applying NLP techniques to corpus linguistics, especially in dealing with large-scale corpora that are beyond the scope of traditional manual analysis. It discusses how models like text classification and text similarity can help address linguistic problems such as categorization (e.g., identifying part-of-speech tags) and comparison (e.g., measuring stylistic similarities between authors).

What I found particularly intriguing is its structure, which is built around five compelling case studies:

  1. Corpus-Based Sociolinguistics: Exploring geographic and social variations in language use.
  2. Corpus Stylistics: Understanding authorship through stylistic differences in texts.
  3. Usage-Based Grammar: Analyzing syntax and semantics via computational models.
  4. Multilingualism Online: Investigating underrepresented languages in digital spaces.
  5. Socioeconomic Indicators: Applying corpus analysis to non-linguistic fields like politics and sentiment in customer reviews.

The book is as much a practical resource as it is theoretical. Accompanied by Python notebooks and a stand-alone Python package, it provides hands-on tools to implement the discussed methods—a feature that makes it especially appealing to readers with a technical bent.

A Personal Connection

My journey with this book is a bit more personal. While exploring NLP, I had the chance to meet Jonathan Dunn, who shared invaluable insights about this field. One of his students, Sidney Wong, recommended this book to me as a starting point for understanding how computational methods can expand corpus linguistics. It has since become a cornerstone of my learning in this area.

What Makes It Unique

Two aspects of Dunn’s book particularly resonated with me:

  1. Ethical Considerations: As corpus sizes grow, so do the ethical dilemmas associated with their use. From privacy issues to biases in computational models, the book doesn’t shy away from discussing the darker side of large-scale text analysis. This balance between innovation and responsibility is a critical takeaway for anyone venturing into NLP.
  2. Interdisciplinary Approach: Whether you’re a linguist looking to incorporate computational methods or a computer scientist aiming to understand linguistic principles, this book bridges the gap between the two disciplines beautifully. It encourages a collaborative perspective, which is essential in fields as expansive as NLP and corpus linguistics.

Who Should Read It?

If you’re a student, researcher, or practitioner with an interest in exploring how NLP can scale linguistic analysis, this book is for you. Its accessibility makes it suitable for beginners, while the advanced discussions and hands-on code offer plenty for seasoned professionals to learn from.

For me, Natural Language Processing for Corpus Linguistics isn’t just a book—it’s a toolkit, a mentor, and an inspiration rolled into one. As I continue my journey in NLP, I find myself revisiting its chapters for insights and ideas.

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