Tag: nlp

How Computational Linguistics Is Powering the Future of Robotics?

As someone who’s been involved in competitive robotics through VEX for several years and recently started diving into computational linguistics, I’ve been wondering: how do these two fields connect?

At first, it didn’t seem obvious. VEX Robotics competitions (like the one my team Ex Machina participated in at Worlds 2025) are mostly about designing, building, and coding autonomous and driver-controlled robots to complete physical tasks. There’s no direct language processing involved… at least not yet. But the more I’ve learned, the more I’ve realized that computational linguistics plays a huge role in making real-world robots smarter, more useful, and more human-friendly.

Here’s what I’ve learned about how these two fields intersect and where robotics is heading.

1. Human-Robot Communication

The most obvious role of computational linguistics in robotics is helping robots understand and respond to human language. This is powered by natural language processing (NLP), a core area of computational linguistics. Think about assistants like Alexa or social robots like Pepper. They rely on language models and parsing techniques to interpret what we say and give meaningful responses.

This goes beyond voice control. It’s about making robots that can hold conversations, answer questions, or even ask for clarification when something is unclear. For robots to work effectively with people, they need language skills, not just motors and sensors.

2. Task Execution and Instruction Following

Another fascinating area is how robots can convert human instructions into actual actions. For example, if someone says, “Pick up the red cup from the table,” a robot must break that down: What object? What location? What action?

This is where semantic parsing comes in—turning language into structured data the robot can use to plan its moves. In VEX, we manually code our autonomous routines, but imagine if a future version of our robot could listen to instructions in plain English and adapt its behavior in real time.

3. Understanding Context and Holding a Conversation

Human communication is complex. We often leave things unsaid, refer to past ideas, or use vague phrases like “that one over there.” Research in discourse modeling and context tracking helps robots manage this complexity.

This is especially useful in collaborative environments. Think hospital robots assisting nurses, or factory robots working alongside people. They need to understand not just commands but also user intent, tone, and changing context.

4. Multimodal Understanding

Robots don’t just rely on language. They also use vision, sensors, and spatial awareness. A good example is interpreting a command like, “Hand me the tool next to the blue box.” The robot has to match those words with what it sees.

This is called multimodal integration, where the robot combines language and visual information. In my own robotics experience, we’ve used vision sensors to detect field elements, but future robots will need to combine that visual input with spoken instructions to act intelligently in dynamic spaces.

5. Emotional and Social Intelligence

This part really surprised me. Sentiment analysis and affective computing are helping robots detect emotions in voice or text, which makes them more socially aware.

This could be important for assistive robots that help the elderly, teach kids, or support people with disabilities. It’s not just about understanding words. It’s about understanding people.

6. Learning from Language

Computational linguistics also helps robots learn and adapt over time. Instead of hardcoding every behavior, researchers are working on ways for robots to learn from manuals, online resources, or natural language feedback.

This is especially exciting as large language models continue to evolve. Imagine a robot reading its own instruction manual or watching a video tutorial and figuring out how to do a new task.

Looking Ahead

While none of this technology is part of the current VEX Robotics competition (at least not yet), understanding how computational linguistics connects to robotics gives me a whole new appreciation for where robotics is going. It also makes me excited about studying this intersection more deeply in college.

Whether it’s through smarter voice assistants, more helpful home robots, or AI systems that respond naturally, computational linguistics is quietly shaping the next generation of robotics.

— Andrew

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WAIC 2025: What Geoffrey Hinton’s “Tiger” Warning Taught Me About AI’s Future

At the end of July (7/26 – 7/28), Shanghai hosted the 2025 World Artificial Intelligence Conference (WAIC), drawing over 1,200 participants from more than 40 countries. Even though I wasn’t there, I followed the conference closely, especially the keynote from Geoffrey Hinton, the so-called “Godfather of AI.” His message? AI is advancing faster than we expect, and we need global cooperation to make sure it stays aligned with human values.

Hinton’s talk was historic. It was his first public appearance in China, and he even stood throughout his address despite back pain, which was noted by local media. One quote really stuck with me: “Humans have grown accustomed to being the most intelligent species in the world – what if that’s no longer the case?” That’s a big question, and as someone who’s diving deeper into computational linguistics and large language models, I felt both amazed and a little uneasy.

His warning compared superintelligent AI to a tiger we’re raising as a pet. If we’re not careful, he said, “the tiger” might one day turn on us. The point wasn’t to scare everyone, but to highlight why we can’t rely on simply pulling the plug if AI systems surpass human intelligence. Hinton believes we need to train AI to be good from the beginning because shutting it down later might not be an option.

WAIC 2025 wasn’t all doom and gloom though. Hinton also talked about the huge potential of AI to accelerate science. For example, he highlighted DeepMind’s AlphaFold as a breakthrough that solved a major biology challenge, predicting protein structures. That shows how powerful AI can be when guided properly.

What stood out the most was the recurring theme of cooperation. Hinton and others, like former Google CEO Eric Schmidt, emphasized the need for global partnerships on AI safety and ethics. Hinton even signed the “Shanghai AI Safety Consensus” with other experts to support international collaboration. The message was clear: no single country can or should handle AI’s future alone.

As a high school student passionate about AI and language, I’m still learning how these pieces fit together. But events like WAIC remind me that the future of AI isn’t just about building smarter systems, it’s also about making sure they work for everyone.

For those interested, here’s a more detailed summary of Hinton’s latest speech: Pandaily Report on WAIC 2025

You can also explore the official WAIC website here: https://www.worldaic.com.cn/

— Andrew

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ACL 2025 New Theme Track: Generalization in NLP Models

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) will be happening in Vienna, Austria from July 27 to August 1. I won’t be attending in person, but as someone planning to study and do research in computational linguistics and NLP in college, I’ve been following the conference closely to keep up with the latest trends.

One exciting thing about this year’s ACL is its new theme track: Generalization of NLP Models. According to the official announcement:

“Following the success of the ACL 2020–2024 Theme tracks, we are happy to announce that ACL 2025 will have a new theme with the goal of reflecting and stimulating discussion about the current state of development of the field of NLP.

Generalization is crucial for ensuring that models behave robustly, reliably, and fairly when making predictions on data different from their training data. Achieving good generalization is critically important for models used in real-world applications, as they should emulate human-like behavior. Humans are known for their ability to generalize well, and models should aspire to this standard.

The theme track invites empirical and theoretical research and position and survey papers reflecting on the Generalization of NLP Models. The possible topics of discussion include (but are not limited to) the following:

  • How can we enhance the generalization of NLP models across various dimensions—compositional, structural, cross-task, cross-lingual, cross-domain, and robustness?
  • What factors affect the generalization of NLP models?
  • What are the most effective methods for evaluating the generalization capabilities of NLP models?
  • While Large Language Models (LLMs) significantly enhance the generalization of NLP models, what are the key limitations of LLMs in this regard?

The theme track submissions can be either long or short. We anticipate having a special session for this theme at the conference and a Thematic Paper Award in addition to other categories of awards.”

This year’s focus on generalization really highlights where the field is going—toward more robust, ethical, and real-world-ready NLP systems. It’s not just about making cool models anymore, but about making sure they work well across different languages, cultures, and use cases.

If you’re into reading papers like I am, especially ones that dig into how NLP systems can perform reliably on new or unexpected inputs, this theme track will be full of insights. I’m looking forward to checking out the accepted papers when they’re released.

You can read more at the official conference page: ACL 2025 Theme Track Announcement

— Andrew

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Attending SCiL 2025: My First In-Person Computational Linguistics Conference at the University of Oregon

This July, I had the amazing opportunity to attend the 2025 Society for Computation in Linguistics (SCiL) conference, held at the University of Oregon in Eugene from July 18 to 20. This wasn’t just my first academic conference in person. It was also my first time attending a conference where I was (surprisingly) the only high school student in the room.

Road Trip to Eugene and My Badge Moment

My family and I made the drive from Seattle to Eugene, a nearly 300-mile road trip along I-5. I was super excited (and a little nervous) to be attending a professional conference alongside professors, postdocs, and graduate students.

When I checked in, I got my conference badge and immediately noticed something funny. My badge just said “Andrew Li,” with no school or organization listed, while everyone else had theirs printed with their university or research institute. I guess Redmond High School isn’t in their system yet!

The Crowd: Grad Students, Professors, and Me

The SCiL crowd was mostly made up of college professors and graduate students. At first, I felt a little out of place sitting in rooms full of experts discussing topics in areas such as pragmatics and large language models. But once the sessions started, I realized that even as a student just starting out in the field, there was so much I could follow and even more that I wanted to learn.

The conference covered a wide range of topics, all tied together by a focus on computational modeling in linguistics. You can find the full conference schedule here.

I was especially drawn to Dr. Malihe Alikhani‘s keynote presentation “Theory of Mind in Generative Models: From Uncertainty to Shared Meaning“. Her talk explored how generative models can effectively facilitate communicative grounding by incorporating theory of mind alongside uncertainty and human feedback. What stood out to me most was the idea that positive friction can be intentionally built into conversational systems so that it encourages contemplative thinking such as reflection on uncertain assumptions by both the users and AI systems. I was also fascinated by how generative
models embody core mechanisms of pragmatic reasoning, offering linguists and cognitive
scientists both methodological challenges and opportunities to question how computational
systems reflect and shape our understanding of meaning and interaction.

Networking and New Connections

While I didn’t get the chance to meet Prof. Jonathan Dunn in person as planned (he’s teaching “Computational Construction Grammar” at the LSA 2025 Summer Institute from July 24 through August 7 and won’t arrive until July 23), I still made some great new connections.

One of them was Andrew Liu, a graduate student at the University of Toronto. We chatted about his project, “Similarity, Transformation, and the Newly Found Invariance of Influence Functions,” which he’s presenting during the poster session. He was super friendly and shared valuable advice about studying and doing research in computational linguistics and NLP. Here’s his LinkedIn profile if you’d like to check out his work.

Talking with grad students made me realize how wide the field of computational linguistics really is. Everyone had a different background — some came from linguistics, others from computer science or cognitive science — but they were all united by a shared passion for understanding language through computation.

Final Thoughts

Attending SCiL 2025 was eye-opening. Even though I was probably the youngest person there, I felt inspired, welcomed, and challenged in the best way. It confirmed my passion for computational linguistics /NLP and reminded me how much more I want to learn.

If you’re a high school student curious about computational linguistics/NLP, don’t be intimidated by professional conferences. Dive in, listen closely, ask questions, and you might be surprised by how much you take away.

— 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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Journals and Conferences for High School Students Interested in Computational Linguistics and NLP

As a high school student interested in studying computational linguistics and natural language processing (NLP) in college, I’ve always looked for ways to stay connected to the latest developments in the field. One of the most effective strategies I’ve found is diving into the world of academic activities: reading papers, following conference proceedings, and even working on papers of my own.

In this post, I’ve put together a list of reputable journals and major conferences in computational linguistics and NLP. These are the publications and venues I wish I had known about when I first started. If you’re just getting into the field, I hope this can serve as a useful starting point.

At the end, I’ve also included a quick update on my recent experiences with two conferences: NAACL 2025 and the upcoming SCiL 2025.

Part I: Journals
Here is a list of prominent journals suitable for publishing research in computational linguistics and natural language processing (NLP), based on their reputation, impact, and relevance to the field:

  1. Computational Linguistics
    • Published by MIT Press for the Association for Computational Linguistics (ACL) since 1988.
    • The primary archival journal for computational linguistics and NLP research, open access since 2009.
    • Focuses on computational and mathematical properties of language and NLP system design.
  2. Transactions of the Association for Computational Linguistics (TACL)
    • Sponsored by the ACL, open access, and archived in the ACL Anthology.
    • Publishes high-quality, peer-reviewed papers in NLP and computational linguistics.
  3. Journal of Machine Learning Research (JMLR)
    • Covers machine learning with some overlap in NLP, including computational linguistics applications.
    • Open access and highly regarded for theoretical and applied machine learning research.
  4. Journal of Artificial Intelligence Research (JAIR)
    • Publishes research in AI, including computational linguistics and NLP topics.
    • Open access with a broad scope in AI-related fields.
  5. Natural Language Engineering
    • Published by Cambridge University Press.
    • Focuses on practical applications of NLP and computational linguistics.
  6. Journal for Language Technology and Computational Linguistics (JLCL)
    • Published by the German Society for Computational Linguistics and Language Technology (GSCL).
    • Covers computational linguistics, language technology, and related topics.
  7. Language Resources and Evaluation
    • Focuses on language resources, evaluation methodologies, and computational linguistics.
    • Published by Springer, often includes papers on corpora and annotation.

Part II: Conferences
The following are the top-tier conferences in computational linguistics and NLP, known for their competitive acceptance rates (often around 25%) and high impact in the field:

  1. Annual Meeting of the Association for Computational Linguistics (ACL)
    • The flagship conference of the ACL, held annually in summer.
    • Covers all aspects of computational linguistics and NLP, highly prestigious.
  2. Empirical Methods in Natural Language Processing (EMNLP)
    • One of the top NLP conferences, focusing on empirical and data-driven NLP research.
    • Held annually.
  3. International Conference on Computational Linguistics (COLING)
    • A major international conference held biennially, covering a broad range of computational linguistics topics.
  4. North American Chapter of the Association for Computational Linguistics (NAACL)
    • The ACL’s North American chapter conference, held annually or biennially.
  5. European Chapter of the Association for Computational Linguistics (EACL)
    • The ACL’s European chapter conference, focusing on NLP research in Europe and beyond.
  6. Conference on Computational Natural Language Learning (CoNLL)
    • Focuses on computational learning approaches to NLP, sponsored by ACL SIGDAT.
    • Known for innovative research in natural language learning.
  7. Lexical and Computational Semantics and Semantic Evaluation (SemEval)
    • A workshop series under ACL, focusing on lexical semantics and evaluation tasks.
    • Highly regarded for shared tasks in NLP.
  8. International Joint Conference on Natural Language Processing (IJCNLP)
    • Held in Asia, often in collaboration with ACL or other organizations.
    • Covers a wide range of NLP topics with a regional focus.
  9. The Society for Computation in Linguistics (SCiL) conference
    • A newer and more specialized event compared to the well-established, top-tier conferences like ACL, EMNLP, COLING, NAACL, and EACL.
    • Began in 2018.
    • Narrower focus on mathematical and computational modeling within linguistics.
    • Frequently held as a sister society meeting alongside the LSA Annual Meeting
  10. Conference on Neural Information Processing Systems (NeurIPS)
    • A premier venue for machine learning research
    • Publish NLP-related papers, however, it is not a dedicated computational linguistics or NLP conference.

Part III: My Experience

NAACL 2025 took place in Albuquerque, New Mexico, from April 29 to May 4, 2025. As you might already know from my previous blog post, one of my co-authored papers was accepted to the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, part of NAACL 2025. Due to a scheduling conflict with school, I wasn’t able to attend in person—but I still participated remotely and followed the sessions virtually. It was an incredible opportunity to see the latest research and learn how experts in the field present and defend their work.

SCiL 2025 will be held from July 18 to July 20 at the University of Oregon, co-located with the LSA Summer Institute. I’ve already registered and am especially excited to meet some of the researchers whose work I’ve been reading. In particular, I’m hoping to connect with Prof. Jonathan Dunn, whose book Natural Language Processing for Corpus Linguistics I mentioned in a previous post. I’ll be sure to share a detailed reflection on the conference once I’m back.

If you’re interested in computational linguistics or NLP—even as a high school student—it’s never too early to start engaging with the academic community. Reading real papers, attending conferences, and publishing your own work can be a great way to learn, connect, and grow.

— Andrew

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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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