---
title: "Pakistan — research institutions, scientists and papers"
description: "Pakistan: 1 institutions with papers in our library in 1 agglomerations, 1 people with a page, 4 works, 1,302 citations."
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---
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# Pakistan

Country 1 institution in 1 agglomeration · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**1** institution with a page](https://md.research.elamur.ai/free/en/countries/pakistan/#institutions) · [**1** agglomeration](https://md.research.elamur.ai/free/en/countries/pakistan/#agglomerations) · [**1** person with a page](https://md.research.elamur.ai/free/en/countries/pakistan/#authors) · [**4** works in our library](https://md.research.elamur.ai/free/en/countries/pakistan/#works) · [**1,302** citations of their works](https://md.research.elamur.ai/free/en/countries/pakistan/#works)

## About

### Profile

Pakistan is represented on this site through a single university in a single city region. Its research here comes from the [University of Engineering and Technology Lahore](https://md.research.elamur.ai/free/en/universities/university-of-engineering-and-technology-lahore/), located in [Lahore](https://md.research.elamur.ai/free/en/agglomerations/lahore/). The country's [institutions](https://md.research.elamur.ai/free/en/countries/pakistan/institutions/) and its [agglomerations](https://md.research.elamur.ai/free/en/countries/pakistan/agglomerations/) on this site therefore describe the same small body of work. Pakistan can also be compared with other entries in the library's index of [countries](https://md.research.elamur.ai/free/en/countries/).

That body of work is concentrated in language technology. Among the works here, Pakistan's contribution covers large language models, machine translation, lexical resources built from WordNets, emotion classification for Arabic text, and parallel corpora for Urdu. Seen from this site, the country's research is a narrow but coherent profile in computational linguistics, carried by one engineering university.

### History

The works here span roughly a dozen years. The earliest is a 2014 study on building an interlingual translation lexicon with WordNets and the Grammatical Framework. Its approach is rooted in rule-based, grammar-driven translation, where hand-structured lexical knowledge links many languages at once.

Nearly a decade separates that study from the next works in our library, and the field had changed in the meantime. In 2023 a broad survey of large language models appeared. It reflects the shift of natural language processing toward large pretrained neural systems. In 2024 a study on Arabic emotion classification used stacked embeddings and a hybrid loss function. It shows the same neural toolkit applied to a specific language and task.

The most recent work, dated 2026, returns to translation, this time for English and Urdu. It focuses on building and evaluating a carefully cleaned parallel corpus. Read together, the works move from symbolic lexicon building, through a survey of the neural era, to resource creation for a language of the region. Throughout, the work keeps returning to the problem of languages with limited digital resources.

### Research profile

By field, most of Pakistan's works here fall under [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/). A smaller group sits on the [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) shelf. Single works also appear under [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) and [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/).

The topic of [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) is represented by [A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/). It is a survey and introduces no new model or corpus. Its value lies in organising a fast-growing literature, and it draws on a very long reference list.

[Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) is the line that runs from the earliest work to the latest. The 2014 lexicon study builds a translation resource from structured lexical databases. The 2026 English–Urdu corpus study builds data for statistical and neural translation systems.

[Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) is the explicit theme of the [English–Urdu parallel corpus study](https://md.research.elamur.ai/free/en/papers/tahir-2026-building-evaluating-high-quality-parallel/). The paper presents its procedure as a recipe that others can reuse. The procedure produces clean, domain-balanced corpora for languages that lack large training collections.

[Sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) and [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) meet in the [Arabic multi-label emotion classification study](https://md.research.elamur.ai/free/en/papers/aslam-2024-improving-arabic-multi-label-emotion/). Multi-label means a single text can carry several emotions at once. The study addresses two difficulties in such problems: labels are imbalanced, and they are correlated with one another. Its combination of stacked embeddings and a hybrid loss is offered as a general method for problems of this kind.

### Leading scientists

The first person with a confirmed page for Pakistan is [Sana Shams](https://md.research.elamur.ai/free/en/authors/sana-shams/) of the University of Engineering and Technology Lahore. Shams has papers in our library and is among the authors of the 2026 study on the English–Urdu parallel corpus. That places Shams's work here in low-resource machine translation, and specifically in building and evaluating translation data for Urdu.

The university's people on this site also include [Salman Khan](https://md.research.elamur.ai/free/en/authors/salman-khan/), [Sarmad Hussain](https://md.research.elamur.ai/free/en/authors/sarmad-hussain/) and [Muhammad Umar Farooq](https://md.research.elamur.ai/free/en/authors/muhammad-umar-farooq/). Their author pages show the works connected with each of them in the library.

Many other names appear in the author lines of the country's works. Humza Naveed, Asad Ullah Khan and Shi Qiu are among the authors of the large language model survey. Shafqat Mumtaz Virk, K.V.S. Prasad and Aarne Ranta are among the authors of the 2014 lexicon study. Muhammad Azeem Aslam, Wang Jun and Nisar Ahmed are among the authors of the Arabic emotion study. Munief Hassan Tahir and Hunain Azam are among the authors of the corpus study, together with Shams.

### Landmark works

By a wide margin, the most cited of Pakistan's works in our library is the 2023 survey of large language models. Many other papers in the library cite it. That makes it the main channel through which research tied to Pakistan reaches the rest of the collection. The survey maps architectures, training approaches and the wider literature without proposing a model of its own. Its citations here suggest it is widely used as a reference point by later work on language models.

Next in citations is the 2014 work, [Developing an interlingual translation lexicon using WordNets and Grammatical Framework](https://md.research.elamur.ai/free/en/papers/virk-2014-developing-interlingual-translation-lexicon-wordnets/). It assembles a multi-sense, full-form lexicon by drawing on Princeton WordNet and the Universal WordNet. The lexicon is designed for use inside the Grammatical Framework, a setting for multilingual grammar-based translation. The study is the clearest example among the works here of symbolic, resource-driven language engineering.

The 2024 Arabic emotion study is the third. It combines several embedding representations and pairs them with a loss function suited to imbalanced, overlapping labels. It is presented as a pattern that can be carried over to similar multi-label tasks.

The 2026 English–Urdu corpus study is the newest work. It already has a first citation on this site. Its contribution is a carefully assembled and evaluated parallel corpus. It also sets out a procedure for building such resources for other under-served languages.

### Labs and units

On this site, Pakistan's research comes from one institution, the University of Engineering and Technology Lahore. It is classed as a university, and it accounts for all of the country's works and citations here. No separate departments, laboratories or research groups are named for it. The institution itself is therefore the unit through which Pakistan's work appears in the library.

### Collaborations

The works here are multi-author papers, and their author lines bring together several researchers. The 2014 lexicon study joins its authors around the Grammatical Framework and WordNet resources. The 2024 Arabic study and the 2023 survey likewise list several authors each. The 2026 corpus study connects Sana Shams with Munief Hassan Tahir and Hunain Azam.

The library does not name the partner institutions or countries of these co-authors. Questions about who works with whom across borders are therefore best followed through the individual works and author pages. Within Pakistan, the library shows only one city region and one university. No links between universities and companies appear here.

### Timeline

In 2014, the earliest work in our library describes an interlingual translation lexicon. It is built from WordNets for the Grammatical Framework, and it marks the start of the country's machine translation line here.

In 2023, the survey of large language models appears. It becomes, by far, the most cited of the country's works in the library and connects its research to the wider literature on pretrained models.

In 2024, a study on Arabic multi-label emotion classification extends the work to sentiment and emotion and to a Semitic language. It does so with stacked embeddings and a hybrid loss.

In 2026, an English–Urdu parallel corpus study, with Sana Shams among its authors, returns to machine translation. It focuses on clean, balanced data for a low-resource language, completing a path from symbolic lexicons to corpus building for Urdu.

## Where

## Agglomerations (1)

1. [**Lahore**1 institution*1,302 citations*](https://md.research.elamur.ai/free/en/agglomerations/lahore/)

## Institutions (1)

### 🎓 Universities (1)

1. **[University of Engineering and Technology Lahore](https://md.research.elamur.ai/free/en/universities/university-of-engineering-and-technology-lahore/)**University · [Lahore](https://md.research.elamur.ai/free/en/agglomerations/lahore/), Pakistan*4 works · 1,302 citations*

## People (1)

Authors with a page confirmed at its institutions, the most cited first.

1. [**Sana Shams**University of Engineering and Technology Lahore*2 papers here · 1 citation*](https://md.research.elamur.ai/free/en/authors/sana-shams/)

## Works in our library (4)

Works of its institutions, the most cited first.

1. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
2. [**Developing an interlingual translation lexicon using WordNets and Grammatical Framework**Shafqat Mumtaz Virk, K.V.S. Prasad, Aarne Ranta, et al.*2014 · 10 citations*](https://md.research.elamur.ai/free/en/papers/virk-2014-developing-interlingual-translation-lexicon-wordnets/)
3. [**Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function**Muhammad Azeem Aslam, Wang Jun, Nisar Ahmed, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/aslam-2024-improving-arabic-multi-label-emotion/)
4. [**Building and Evaluating a High Quality Parallel Corpus for English Urdu Low Resource Machine Translation**Munief Hassan Tahir, Hunain Azam, Sana Shams, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tahir-2026-building-evaluating-high-quality-parallel/)

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7[Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 3 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Experimental and Cognitive Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/)

🧭 Topics 6[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Machine translation · 2 works](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Semitic languages · 1 work](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · [Sentiment & emotion · 1 work](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [English · 1 work](https://md.research.elamur.ai/free/en/topics/english/) · [Low-resource languages · 1 work](https://md.research.elamur.ai/free/en/topics/low-resource-languages/)

## Works by year

### 2020s

[**2026** 1 work](https://md.research.elamur.ai/free/en/papers/tahir-2026-building-evaluating-high-quality-parallel/) · [**2024** 1 work](https://md.research.elamur.ai/free/en/papers/aslam-2024-improving-arabic-multi-label-emotion/) · [**2023** 1 work](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)

### 2010s

[**2014** 1 work](https://md.research.elamur.ai/free/en/papers/virk-2014-developing-interlingual-translation-lexicon-wordnets/)

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Updated 23 October 2026