The paper presents our system developed for SemEval-2025 Task 8, which focuses on table question answering (TQA). The TQA tasks face challenges due to the characteristics of real-world tabular data, such as large size, incomplete column semantics, and entity ambiguity. To address these issues, we propose a large language model (LLM)-powered and programming-based framework, named Flow-of-Table-Reasoning. We introduce the table schema integrating verbalized structure and semantics for query decomposition and programming, enabling a holistic understanding of tables and the ability to process large-size tables. We design a multi-step schema linking plan to derive a focused table schema that retains only information relevant to the query, aiming to eliminate ambiguity and reduce hallucinations. Furthermore, we incorporate reasoning workflow into an iterative thinking architecture, allowing incremental cycles of thinking, reasoning and reflection. Our system achieves first place on both TQA and Lite TQA subtasks.

As a precious cultural heritage of human beings, classical Chinese poetry has a very unique writing style and often contains special words that rarely appear in general Chinese texts, posting critical challenges for natural language processing. Little effort has been made in the literature for processing texts from classical Chinese poetry. This study fills in this gap with TopWORDS-Poetry, an unsupervised method that can achieve reliable text segmentation and word discovery for classical Chinese poetry simultaneously without pre-given vocabulary or training corpus. Experimental studies confirm that TopWORDS-Poetry can successfully recognize unique poetry words, such as named entities and literary allusions, from metrical poems of Complete Tang Poetry and segment these poetry lines into sequences of meaningful words with high quality.

Processing open-domain Chinese texts has been a critical bottleneck in computational linguistics for decades, partially because text segmentation and word discovery often entangle with each other in this challenging scenario. No existing methods yet can achieve effective text segmentation and word discovery simultaneously in open domain. This study fills in this gap by proposing a novel method called TopWORDS-Seg based on Bayesian inference, which enjoys robust performance and transparent interpretation when no training corpus and domain vocabulary are available. Advantages of TopWORDS-Seg are demonstrated by a series of experimental studies.