As interest grows in the use of linguistically annotated corpora in research and teaching of foreign languages and literature, treebanks of various historical texts have been developed. We introduce the first large-scale dependency treebank for Classical Chinese literature. Derived from the Stanford dependency types, it consists of over 32K characters drawn from a collection of poems written in the 8th century CE. We report on the design of new dependency relations, discuss aspects of the annotation process and evaluation, and illustrate its use in a study of parallelism in Classical Chinese poetry.

http://www2.ctl.cityu.edu.hk/~jsylee/publications/N12-1020.pdf

A Dependency Treebank of Classical Chinese Poems

We introduce a corpus of classical Chinese poems that has been word segmented and tagged with parts-of-speech (POS). Due to the ill-defined concept of a 'word' in Chinese, previous Chinese corpora suffer from a lack of standardization in word segmentation, resulting in inconsistencies in POS tags, therefore hindering interoperability among corpora. We address this problem with nested POS tags, which accommodates different theories of wordhood and facilitates research objectives requiring annotations of the 'word' at different levels of granularity.

http://www2.ctl.cityu.edu.hk/~jsylee/publications/W12-1011.pdf

A Classical Chinese Corpus with Nested Part-of-Speech Tags

While our knowledge about ancient civilizations comes mostly from studies in archaeology and history books, much can also be learned or confirmed from literary texts . Using natural language processing techniques, we present aspects of ancient China as revealed by statistical textual analysis on the Complete Tang Poems, a 2.6-million-character corpus of all surviving poems from the Tang Dynasty (AD 618—907). Using an automatically created treebank of this corpus , we outline the semantic profiles of various poets, and discuss the role of s easons, geography, history, architecture, and colours , as observed through word selection and dependencies.

http://www2.lt.cityu.edu.hk/~jsylee/publications/coling2012.pdf

Glimpses of Ancient China from Classical Chinese Poems

Classical Chinese is essentially different from Modern Chinese, in both syntax and morphology. While there has recently been a number of works on part-of-speech (PoS) tagging for Modern Chinese, the PoS tagging for Classical Chinese is largely neglected. To the best of our knowledge, this is the first work in the area. Fortunately however, in terms of tagging, Classical Chinese is easier than Modern Chinese in that most Classical Chinese words are single-character-formed, thus no segmentation is needed. So in this paper, we will propose and analyze a simple statistical approach for PoS tagging of Classical Chinese. We first designed a tagset for Classical Chinese that is later shown to be accurate and efficient. Then we apply the hidden Markov model (HMM) Viterbi algorithm and made several improvements, such as sparse data problem handling and unknown word guessing, both designed particularly for Classical Chinese. As the training set grows larger, the accuracies for bigram and trigram increase to 94.9% and 97.6%, respectively. The contribution of our work also lies in proposing and solving some previously unseen problems in processing Classical Chinese.

Statistical Part-of-Speech Tagging for Classical Chinese

Classical Chinese is essentially different from Modern Chinese, in both syntax and morphology. While there has recently been a number of works on part-of-speech (PoS) tagging for Modern Chinese, the PoS tagging for Classical Chinese is largely neglected. To the best of our knowledge, this is the first work in the area. Fortunately however, in terms of tagging, Classical Chinese is easier than Modern Chinese in that most Classical Chinese words are single-character-formed, thus no segmentation is needed. So in this paper, we will propose and analyze a simple statistical approach for PoS tagging of Classical Chinese. We first designed a tagset for Classical Chinese that is later shown to be accurate and efficient. Then we apply the hidden Markov model (HMM) Viterbi algorithm and made several improvements, such as sparse data problem handling and unknown word guessing, both designed particularly for Classical Chinese. As the training set grows larger, the accuracies for bigram and trigram increase to 94.9 % and 97.6 %, respectively. The contribution of our work also lies in proposing and solving some previously unseen problems in processing Classical Chinese.

Statistical part-of-speech tagging for Classical Chinese

The Academia Sinica Ancient Chinese Corpus is designed for linguistic research. The corpus contains ancient texts that are selected because of their usefulness in grammatical and lexical studies, as well as an inspection program with keyword searching, statistics, and collocation functions. The corpus is divided into three subcorpora according to stages of grammatical developments, thus both synchronic and diachronic studies can be performed on them. Their current sizes are as follows: * E-Mail: Weipc@pluto.ihp.sinica.tw + ** 131

建構一個以共時與歷時語言研究為導向的歷史語料庫 (Historical Corpora for Synchronic and Diachronic Linguistics Studies) [In Chinese]

建構一個以共時與歷時語言研究為導向的歷史語料庫 (Historical Corpora for Synchronic and Diachronic Linguistics Studies).

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建構一個以共時與歷時語言研究為導向的歷史語料庫 (Historical Corpora for Synchronic and Diachronic Linguistics Studies) [In Chinese]

建構一個以共時與歷時語言研究為導向的歷史語料庫 (Historical Corpora for Synchronic and Diachronic Linguistics Studies).