“This paper addresses the task of identifying and classifying historical event trigger words in Classical Chinese, utilizing both small-scale and large-scale language models. Specifically, we selected the small-scale language model GujiBERT for intelligent processing of classical texts, and the large-scale language model Xunzi-Qwen-14b. Both models underwent continued pretraining and fine-tuning, resulting in GujiBERT-CHED-mlm and Xunzi-Qwen-14b-CHED. For the small-scale language model, we used a BiLSTM as the feature extraction module and a CRF as the decoding module, employing a sequence labeling paradigm to complete the evaluation experiments. For the large-scale language model, we optimized the prompt templates and used a sequence-to-sequence paradigm for evaluation experiments. Our experiments revealed that GujiBERT-BiLSTM-CRF achieved the best performance across all tasks, ranking fourth in overall performance among all participating teams. The large-scale language model demonstrated good semantic understanding abilities, reaching a preliminary usable level. Future research should focus on enhancing its ability to produce standardized outputs.”

With the appearance of Semantic Web technologies, it becomes possible to develop novel, sophisticated question answering systems, where ontologies are usually used as the core knowledge component. In the EU-funded project, QALL-ME, a domain-specific ontology was developed and applied for question answering in the domain of tourism, along with the assistance of two upper ontologies for concept expansion and reasoning. This paper focuses on the development of the QALL-ME ontology in the tourism domain and its alignment with the upper ontologies - WordNet and SUMO. The design of the ontology is presented in the paper, and a semi-automatic alignment procedure is described with some alignment results given as well. Furthermore, the aligned ontology was used to semantically annotate original data obtained from the tourism web sites and natural language questions. The storage schema of the annotated data and the data access method for retrieving answers from the annotated data are also reported in the paper.