Ancient Chinese books have great values in history and cultural studies. Named en-tities like person, location, time are cru-cial elements, thus automatic Named En-tity Recognition (NER) is considered a ba-sic task in ancient Chinese text processing. This paper introduces EvaHan2025, the first international ancient Chinese Named Entity Recognition bake-off. The evalua-tion introduces a rigorous benchmark for assessing NER performance across histori-cal and medical texts, covering 12 named entity types. A total of 13 teams par-ticipated in the competition, submitting 77 system runs. In the closed modality, where participants were restricted to us-ing only the training data, the highest F1 scores reached 85.04% on TestA and 90.28% on TestB, both derived from his-torical texts, while performance on medi-cal texts (TestC) reached 84.49%. The re-sults indicate that text genre significantly impacts model performance, with histori-cal texts generally yielding higher scores. Additionally, the intrinsic characteristics of named entities also influence recogni-tion performance. These findings high-light the challenges and opportunities in ancient Chinese NER and underscore the importance of domain adaptation and en-tity type diversity in future research.

“篇章共指体现篇章概念的动态转移,成为近年研究热点。本文在梳理共指理论研究的基础上,综述了相关语料库及解析方法,发现共指语料库仍存在以下两个问题:共指关系标注粗疏与基本不考虑整句语义表示的融合。本文以句子级语义标注体系(中文抽象语义表示)为基础构建篇章共指体系,构建了 100 篇共指语料库。本体系涵盖 52 种句内语义关系和 8 种篇章共指关系,二者相结合构建的篇章共指语义图,为篇章级语义分析提供新的框架和数据资源。”

“Abstract Meaning Representation has become a key research area in sentence-level semantic parsing within natural language processing. Substantial progress has been achieved in various NLP tasks using AMR. This paper presents the fourth Chinese Abstract Meaning Representation parsing evaluation, held during the technical evaluation task workshop at CCL 2024. The evaluation also introduced a new test set comprising Ancient Chinese sentences. Results indicated decent performance, with the top team achieving an F1 of 0.8382 in the open modality, surpassing the previous record at CoNLL 2020 by 3.30 percentage points under the MRP metric. However, current large language models perform poorly in AMR parsing of Ancient Chinese, highlighting the need for effective training strategies. The complex syntax and semantics of Ancient Chinese pose significant challenges. Additionally, optimizing transfer learning techniques to better apply knowledge from Chinese Mandarin to Ancient Chinese parsing is crucial. Only through continuous innovation and collaboration can significant advancements in both Ancient Chinese and Chinese Mandarin AMR parsing be achieved.”

Ancient Chinese texts have no sentence boundaries and punctuation. Adding modern Chinese punctuation to theses texts requires expertise, time and efforts. Automatic sentence segmentation and punctuation is considered as a basic task for Ancient Chinese processing, but there is no shared task to evaluate the performances of different systems. This paper presents the results of the first ancient Chinese sentence segmentation and punctuation bakeoff, which is held at the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2024. The contest uses metrics for detailed evaluations of 4 genres of unpublished texts with 11 punctuation types. Six teams submitted 32 running results. In the closed modality, the participants are only allowed to use the training data, the highest obtained F1 scores are respectively 88.47% and 75.29% in sentence segmentation and sentence punctuation. The perfermances on the unseen data is 10 percent lower than the published common data, which means there is still space for further improvement. The large language models outperform the traditional models, but LLM changes the original characters around 1-2%, due to over-generation. Thus, post-processing is needed to keep the text consistancy.

The digitization of ancient books necessitates the implementation of automatic word segmentation and part-of-speech tagging. However, the existing research on this topic encounters pressing issues, including suboptimal efficiency and precision, which require immediate resolution. This study employs a methodology that combines word segmentation and part-of-speech tagging. It establishes a correlation between fonts and radicals, trains the Radical2Vec radical vector representation model, and integrates it with the SikuRoBERTa word vector representation model. Finally, it connects the BiLSTM-CRF neural network.The study investigates the combination of word segmentation and part-of-speech tagging through an experimental approach using a specific data set. In the evaluation dataset, the F1 score for word segmentation is 95.75%, indicating a high level of accuracy. Similarly, the F1 score for part-of-speech tagging is 91.65%, suggesting a satisfactory performance in this task. This model enhances the efficiency and precision of the processing of ancient books, thereby facilitating the advancement of digitization efforts for ancient books and ensuring the preservation and advancement of ancient book heritage.

“Abstract Meaning Representation has emerged as a prominent area of research in sentence-levelsemantic parsing within the field of natural language processing in recent years. Substantialprogress has been made in various NLP subtasks through the application of AMR. This paperpresents the third Chinese Abstract Meaning Representation Parsing Evaluation, held as part ofthe Technical Evaluation Task Workshop at the 22nd Chinese Computational Linguistics Confer-ence. The evaluation was specifically tailored for the Chinese and utilized the Align-smatch met-ric as the standard evaluation criterion. Building upon high-quality semantic annotation schemesand annotated corpora, this evaluation introduced a new test set comprising interrogative sen-tences for comprehensive evaluation. The results of the evaluation, as measured by the F-score,indicate notable performance achievements. The top-performing team attained a score of 0.8137in the closed test and 0.8261 in the open test, respectively, using the Align-smatch metric. No-tably, the leading result surpassed the SOTA performance at CoNLL 2020 by 3.64 percentagepoints when evaluated using the MRP metric. Further analysis revealed that this significantprogress primarily stemmed from improved relation prediction between concepts. However, thechallenge of effectively utilizing semantic relation alignments remains an area that requires fur-ther enhancement.”

Abstract Meaning Representation is a sentence-level meaning representation, which abstracts the meaning of sentences into a rooted acyclic directed graph. With the continuous expansion of Chinese AMR corpus, more and more scholars have developed parsing systems to automatically parse sentences into Chinese AMR. However, the current parsers can’t deal with concept alignment and relation alignment, let alone the evaluation methods for AMR parsing. Therefore, to make up for the vacancy of Chinese AMR parsing evaluation methods, based on AMR evaluation metric smatch, we have improved the algorithm of generating triples so that to make it compatible with concept alignment and relation alignment. Finally, we obtain a new integrity metric align-smatch for paring evaluation. A comparative research then was conducted on 20 manually annotated AMR and gold AMR, with the result that align-smatch works well in alignments and more robust in evaluating arcs. We also put forward some fine-grained metric for evaluating concept alignment, relation alignment and implicit concepts, in order to further measure parsers’ performance in subtasks.