Latent Probabilistic Topic Discovery for Text Documents
Incorporating Segment Structure and Word Order.
A Game, a Cliff, and a Question About Reading.
Not all jumps are equal.
Growing up on Super Mario, every player learns this without being told. Two nearby, low ledges? Trivial. Move the platform farther away or higher up and it suddenly demands real precision.
Distance and height are what make a jump hard.
Reading is the same terrain.
Some sentences are short hops between everyday words. Others make you leap a wide gap between unfamiliar concepts, or climb onto a ledge of dense technical vocabulary.
Conceptual distance and vocabulary height are what make text hard.
So model the document as terrain.
Rank text by difficulty by measuring how hard the jumps across it are. That became my first CIKM paper — and the seed of an entire PhD on structure-aware topic models.
Play the terrain below. It is that mental picture, made literal.
Farther cliffs, higher cliffs, harder reading.
Each ledge below is a sentence. Click a ledge — or use the controls — to make the explorer jump to it. The height and distance of the jump map directly onto how much cognitive load that sentence demands from a reader.
Pioneering Word Order Modeling.
My thesis challenged the traditional "Bag-of-Words" assumption that dominated NLP for decades. By introducing non-parametric segment structure and word order dependency, I developed models that could capture the actual narrative flow of human language, leading to a Best Research Awards nomination across all of Hong Kong.
Foundation for Deep Learning.
The concepts explored in my doctoral work—specifically how segments and sequences define meaning—foreshadowed the architectural shift toward modern Transformers and Large Language Models (LLMs). My algorithms proved that structure is as vital as statistics in high-stakes information retrieval.
An Unsupervised Ranking Method Based on a Technical Difficulty Terrain
Shoaib Jameel, Wai Lam, Ching-man Au Yeung, and Sheaujiun Chyan. CIKM 2011, pp. 1989–1992, Glasgow, Scotland. Winner: Best Paper Award, Beijing–Hong Kong International Doctoral Forum 2011.
DOI: 10.1145/2063576.2063872- ▹ Non-parametric N-Gram Topic Models: Developed a framework that automatically discovers the appropriate length and complexity of phrases without pre-defined constraints.
- ▹ Segment Structure Integration: Pioneered the use of document internal boundaries to improve the interpretability and coherence of latent topics.
- ▹ Sequential Discourse Cohesion: Created ranking models that understand how conceptual difficulty transitions across a text, a vital component for domain-specific readability.