Tag: computational-linguistics

The AI Gap: How Socioeconomic Status Shapes Language Technology Use — A Perspective from Best Social Impact Paper at ACL 2025

The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) recently finished in Vienna, Austria from July 27 to August 1. The conference announced a few awards, one of which is Best Social Impact Paper. This award was given to two papers:

  1. AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (by Charles Nimo et al.)
  2. The AI Gap: How Socioeconomic Status Affects Language Technology Interactions (by Elisa Bassignana, Amanda Cercas Curry, and Dirk Hovy).

In this blog post, I’ll talk about the second paper and share the findings from the paper and my thoughts on the topic. You can read the full paper here: https://aclanthology.org/2025.acl-long.914.pdf

What the Paper is About

This paper investigates how socioeconomic status (SES) influences interactions with language technologies, particularly large language models (LLMs) like ChatGPT, highlighting an emerging “AI Gap” that could exacerbate social inequalities. Drawing from the Technology Acceptance Model and prior work on digital divides, the authors argue that SES shapes technology adoption through factors like access, digital literacy, and linguistic habits, potentially biasing LLMs toward higher-SES patterns and underrepresenting lower-SES users.

Methods

The study surveys 1,000 English-speaking participants from the UK and US via Prolific, stratified by self-reported SES using the MacArthur scale (binned as low: 1-3, middle: 4-7, upper: 8-10). It collects sociodemographic data, usage patterns of language technologies (e.g., spell checkers, AI chatbots), and 6,482 real prompts from prior LLM interactions. Analysis includes statistical tests (e.g., chi-square for usage differences), linguistic metrics (e.g., prompt length, concreteness via Brysbaert et al.’s word ratings), topic modeling (using embeddings, UMAP, HDBSCAN, and GPT-4 for cluster descriptions), and markers of anthropomorphism (e.g., phatic expressions like “hi” and politeness markers like “thank you”).

Key Findings

  • Usage Patterns: Higher-SES individuals access more devices daily (e.g., laptops, smartwatches) and use LLMs more frequently (e.g., daily vs. rarely for lower SES). They employ LLMs for work/education (e.g., coding, data analysis, writing) and technical contexts, while lower-SES users favor entertainment, brainstorming, and general knowledge queries. Statistically significant differences exist in frequency (p < 0.001), contexts (p < 0.001), and tasks (p < 0.001).
  • Linguistic Differences in Prompts: Higher-SES prompts are shorter (avg. 18.4 words vs. 27.0 for low SES; p < 0.05) and more abstract (concreteness score: 2.57 vs. 2.66; p < 0.05). Lower-SES prompts show higher anthropomorphism (e.g., more phatic expressions) and concrete language. A bag-of-words classifier distinguishes SES groups (Macro-F1 39.25 vs. baseline 25.02).
  • Topics and Framing: Common topics (e.g., translation, mental health, medical advice, writing, text editing, finance, job, food) appear across groups, but framing varies—e.g., lower SES seeks debt reduction or low-skill jobs; higher SES focuses on investments, travel itineraries, or inclusivity. About 45% of prompts resemble search-engine queries, suggesting LLMs are replacing traditional searches.
  • User Perceptions: Trends indicate lower-SES users anthropomorphize more (e.g., metaphorical verbs like “ask”), while higher-SES use jargon (e.g., “generate”), though not statistically significant.

Discussion and Implications

The findings underscore how SES stratifies LLM use, with higher-SES benefiting more in professional/educational contexts, potentially widening inequalities as LLMs optimize for their patterns. Benchmarks may overlook lower-SES styles, leading to biases. The authors advocate the development of inclusive NLP technologies to accommodate different SES needs and habitus and mitigate the existing AI Gap.

Limitations and Ethics

Limited to Prolific crowdworkers (skewed middle/low SES, tech-savvy), subjective SES measures, and potential LLM-generated responses. Ethical compliance includes GDPR anonymity, opt-outs, and fair compensation (£9/hour).

Overall, the paper reveals SES-driven disparities in technology interactions, urging NLP development to address linguistic and habitual differences for equitable access and reduced digital divides.

My Takeaway

As a high school student who spends a lot of time thinking about fairness in AI, I find this paper important because it reminds us that bias is not just about language or culture, it can also be tied to socioeconomic status. This is something I had not thought much about before. If AI systems are trained mostly on data from higher SES groups, they might misunderstand or underperform for people from lower SES backgrounds. That could affect how well people can use AI for education, job searching, or even just getting accurate information online.

For me, the takeaway is that AI researchers need to test their models with SES diversity in mind, just like they do with gender or language diversity. And as someone interested in computational linguistics, it is inspiring to see that work like this is getting recognized with awards at ACL.

— Andrew

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Reflections on Andrew Ng’s Tip: Building Small AI Projects and Its Implications for Computational Linguistics Research

Recently, I read the latest greeting from Andrew Ng in The Batch (Issue #308), where he shared a tip about getting more practice building with AI. His advice really resonated with me, especially as someone exploring computational linguistics research while balancing schoolwork and robotics competitions.

Andrew Ng’s Key Advice

In his post, Andrew Ng emphasized:

If you find yourself with only limited time to build, reduce the scope of your project until you can build something in whatever time you do have.

He shared how he often cuts down an idea into the smallest possible component he can build in an hour or two, rather than waiting for a free weekend or months to tackle the entire project. He illustrated this with his example of creating an audience simulator for practicing public speaking. Instead of building a complex multi-person AI-powered simulation, he started by creating a simple 2D avatar with limited animations that could be expanded later.

Implications for Computational Linguistics Research

Reading this made me think about how I often approach my own computational linguistics projects. Here are a few reflections:

1. Start Small with Linguistic Tasks

In computational linguistics, tasks can feel overwhelming. For example, creating a full sentiment analysis pipeline for multiple languages, building a neural machine translation system, or training large language models are all massive goals.

Andrew Ng’s advice reminds me that it’s okay — and often smarter — to start with a small, well-defined subtask:

  • Instead of building a multilingual parser, start by training a simple POS tagger on a small dataset.
  • Instead of designing a robust speech recognition system, start by building a phoneme classifier for a single speaker dataset.
  • Instead of developing an entire chatbot pipeline, start by implementing a rule-based intent recognizer for a specific question type.

2. Build Prototypes to Test Feasibility

His example of building a minimal audience simulator prototype to get feedback also applies to NLP. For instance, if I want to work on dialect detection on Twitch chat data (something I’ve thought about), I could first build a prototype classifier distinguishing only two dialects or language varieties. Even if it uses basic logistic regression with TF-IDF features, it tests feasibility and lets me get feedback from mentors or peers before expanding.

3. Overcome Perfection Paralysis

As a student, I sometimes hold back on starting a project because I feel I don’t have time to make it perfect. Andrew Ng’s advice to reduce the project scope until you can build something right away is a mindset shift. Even a basic script that tokenizes Twitch messages or parses sentence structures is progress.

4. Practicing Broad Skills by Hacking Small Projects

He also mentioned that building many small projects helps practice a wide range of skills. In computational linguistics, that could mean:

  • Practicing different Python NLP libraries (NLTK, spaCy, Hugging Face)
  • Trying out rule-based vs. machine learning vs. deep learning approaches
  • Exploring new datasets and annotation schemes

Final Thoughts

I really appreciate Andrew Ng’s practical mindset for builders. His advice feels especially relevant to computational linguistics, where small wins accumulate into larger research contributions. Instead of feeling blocked by the scale of a project, I want to keep practicing the art of scoping down and just building something small but meaningful.

If you’re also working on computational linguistics or NLP projects as a student, I hope this inspires you to pick a tiny subtask today and start building.

Let me know if you want me to share a future post listing some small NLP project ideas that I’m working on this summer.

— Andrew

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Speeding Up AI for Everyone: The PaPaformer Model Making Language Tech Work on Phones and Low-Power Devices

AI has become more capable than ever, but many of the most advanced tools still require massive cloud servers to run. That means if you want ChatGPT-level performance, you usually need a reliable internet connection and a lot of computing power behind the scenes. But what if you could have that kind of AI right on your phone, even without Wi‑Fi?

That’s where the PaPaformer model comes in.

What is the PaPaformer Model?
PaPaformer is a new AI architecture developed to train large language models more efficiently and make them small enough to run smoothly on low-power devices like smartphones, tablets, or even embedded systems. You can read more about it in the original paper here: PaPaformer: Language Model from Pre-trained Parallel Paths.

Unlike most large models today that require powerful cloud servers to process requests, PaPaformer is designed so the model can be stored and run directly on your device. This means you can use advanced language technology without a constant internet connection. It also helps protect privacy, since your data stays local instead of being sent to the cloud for processing.

Why It Matters
By making AI lighter and more portable, PaPaformer could bring powerful language tools to more people around the world, including those with limited internet access or older devices. It could also make AI faster to respond, since it does not have to constantly send data back and forth to the cloud.

Examples in Action
Imagine using ChatGPT-style features on a budget smartphone in a remote area. With most current apps, like the regular ChatGPT app, you still need a strong internet connection because the AI runs on servers, not your device. But with a PaPaformer-powered tool, the AI would actually run locally, meaning you could:

  • Translate between languages instantly, even without Wi‑Fi
  • Use a speech-to-text tool for endangered languages that works entirely on your device
  • Let teachers translate lessons in real time for students in rural schools without relying on an internet connection
  • Help students write essays in multiple languages privately, without sending drafts to a remote server

This offline capability is the big difference. It is not just accessing AI through the cloud, it is carrying the AI with you wherever you go.

Looking Ahead
If PaPaformer and similar approaches keep improving, we could see a future where advanced AI is available to anyone, anywhere, without needing expensive devices or constant internet access. For someone like me, interested in computational linguistics, this could also open up new possibilities for preserving languages, creating translation tools, and making language technology more inclusive worldwide.

— Andrew

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How NLP Helps Robots Handle Interruptions: A Summary of JHU Research

I recently came across an awesome study from Johns Hopkins University describing how computational linguistics and NLP can make robots better conversational partners by teaching them how to handle interruptions, a feature that feels basic for humans but is surprisingly hard for machines.

What the Study Found

Researchers trained a social robot powered by a large language model (LLM) to manage real-time interruptions based on speaker intent. They categorized interruptions into four types: Agreement, Assistance, Clarification, and Disruption.

By analyzing human conversations from interviews to informal discussions, they designed strategies tailored to each interruption type. For example:

  • If someone agrees or helps, the robot pauses, nods, and resumes speaking.
  • When someone asks for clarification, the robot explains and continues.
  • For disruptive interruptions, the robot can either hold the floor to summarize its remaining points before yielding to the human user, or it can stop talking immediately.

How NLP Powers This System

The robot uses an LLM to:

  1. Detect overlapping speech
  2. Classify the interrupter’s intent
  3. Select the appropriate response strategy

In tests involving tasks and conversations, the system correctly interpreted interruptions about 89% of the time and responded appropriately 93.7% of the time.

Why This Matters in NLP and Computational Linguistics

This work highlights how computational linguistics and NLP are essential to human-robot interaction.

  • NLP does more than generate responses; it helps robots understand nuance, context, and intent.
  • Developing systems like this requires understanding pause cues, intonation, and conversational flow, all core to computational linguistics.
  • It shows how multimodal AI, combining language with behavior, can enable more natural and effective interactions.

What I Found Most Interesting

The researchers noted that users didn’t like when the robot “held the floor” too long during disruptive interruptions. It reminded me how pragmatic context matters. Just like people expect some rules in human conversations, robots need these conversational skills too.

Looking Ahead

This research expands what NLP can do in real-world settings like healthcare, education, and social assistants. For someone like me who loves robots and language, it shows how computational linguistics helps build smarter, more human-friendly AI systems.

If you want to dive deeper, check out the full report from Johns Hopkins:
Talking robots learn to manage human interruptions

— Andrew

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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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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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Summer Programs and Activities in Computational Linguistics: My Personal Experiences and Recommendations

If you’re a high school student interested in computational linguistics, you might be wondering: What are some ways to dive deeper into this field over the summer? As someone who loves language, AI, and everything in between, I’ve spent the past year researching programs and activities, and I wanted to share what I’ve learned (along with some of my personal experiences).

1. Summer Linguistic Institute for Youth Scholars (SLIYS)

What it is:
SLIYS is a two-week summer program run by The Ohio State University’s Department of Linguistics. It focuses on introducing high school students to language analysis and linguistic theory in a fun and rigorous way. Students get to explore syntax, morphology, phonetics, language universals, and even some computational topics.

My experience:
I’m super excited to share that I’ll be participating in SLIYS this summer (July 14 – 25, 2025). I was so happy to be accepted, and I’m looking forward to learning from real linguistics professors and meeting other students who are passionate about language. I’ll definitely share a reflection post after I finish the program, so stay tuned if you want an inside look!

Learn more about SLIYS here.

2. Summer Youth Camp for Computational Linguistics (SYCCL)

What it is:
SYCCL is a summer camp hosted by the Department of Linguistics and the Institute for Advanced Computational Science at Stony Brook University. It introduces high school students to computational linguistics and language technology, covering topics like language data, NLP tools, and coding for language analysis.

My experience:
I had planned to apply for SYCCL this year as well, but unfortunately, its schedule (July 6 – 18, 2025) conflicted with SLIYS, which I had already accepted. Another challenge I faced was that SYCCL’s website wasn’t updated until late April 2025, which is quite late compared to other summer programs. I had actually contacted the university earlier this year and they confirmed it would run again, but I didn’t see the application open until April. My advice is to check their website frequently starting early spring, and plan for potential conflicts with other summer programs.

Learn more about SYCCL here.

3. North American Computational Linguistics Open Competition (NACLO)

What it is:
NACLO is an annual computational linguistics competition for high school students across North America. It challenges students with problems in linguistics and language data analysis, testing their ability to decipher patterns in unfamiliar languages.

My experience:
I’ve tried twice to participate in NACLO at my local test center. Unfortunately, both times the test dates were weekdays that conflicted with my school final exams, so I had to miss them. If you’re planning to participate, I strongly recommend checking the schedule early to make sure it doesn’t overlap with finals or other major commitments. Despite missing it, I still find their practice problems online really fun and useful for thinking like a computational linguist.

Learn more about NACLO here.

4. LSA Summer Institute

What it is:
The Linguistic Society of America (LSA) Summer Institute is an intensive four-week program held every two years at different universities. It offers courses and workshops taught by top linguists and is known as one of the best ways to explore advanced topics in linguistics, including computational linguistics.

My experience:
I was planning to apply for the LSA Summer Institute this year. However, I found out that it is only open to individuals aged 18 and older. I contacted the LSA Institute Registration Office to ask if there could be any exceptions or special considerations for underage participants, but it was disappointing to receive their response: “Unfortunately, the age limit is firm and the organizers will not be considering any exceptions.” So if you’re thinking about applying, my advice is to check the age qualifications early before starting the application process.

Learn more about LSA Summer Institute here.

5. Local University Outreach Events and Courses

Another great way to explore linguistics and computational linguistics is by checking out courses or outreach events at local universities. For example, last summer I took LING 234 (Language and Diversity) at the University of Washington (Seattle). It was an eye-opening experience to study language variation, identity, and society from a college-level perspective. I wrote a reflection about it in my blog post from November 29, 2024. If your local universities offer summer courses for high school students, I highly recommend checking them out.

6. University-Affiliated AI4ALL Summer Programs for High School Students

What it is:
AI4ALL partners with universities to offer summer programs introducing high school students to AI research, ethics, and applications, often including NLP and language technology projects. While these programs are not focused solely on computational linguistics, they provide a great entry point into AI and machine learning, which are essential tools for language technology research.

About AI4ALL:
AI4ALL is a U.S.-based nonprofit focused on increasing diversity and inclusion in artificial intelligence (AI) education, research, development, and policy, particularly for historically underrepresented groups such as Black, Hispanic/Latinx, Indigenous, women, non-binary, low-income, and first-generation college students. Their mission is to make sure the next generation of AI researchers and developers reflects the diversity of the world.

Examples:

  • Stanford AI4ALL
  • Princeton AI4ALL
  • Carnegie Mellon AI4ALL

These programs are competitive and have different focus areas, but all aim to broaden participation in AI by empowering future researchers early.

Final Thoughts

I feel grateful to have these opportunities to grow my passion for computational linguistics, and I hope this list helps you plan your own summer learning journey. Whether you’re solving NACLO problems in your free time or spending two weeks at SLIYS like I will this summer, every step brings you closer to understanding how language and AI connect.

Let me know if you want a future post reviewing SLIYS after I complete it in July!

— Andrew

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