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Description Magi Open Information Extraction Dataset (MOIED) is a Chinese Open IE dataset containing 7,618,181 records extracted from plain text across 3,319,763 webpages in various domains. Each record in the dataset consists of the (subject, predicate, object) tuple, the associated confidence score, and the context information. The dataset comprises 1,427,742 distinct facts of 272,522 entities and 117,731 predicates. A notable property of MOIED is that each distinct fact has multiple records with URLs referring to mentions in diverse contexts, which enables multiple-instance learning (MIL) and other correlative approaches. As a paragraph level Open IE dataset, at least 45.1% of the records in MOIED can only be extracted through synthesizing information from multiple sentences. Magi is an extraction engine that continuously learns from the Internet, which combines cross-referencing, timeline analysis, and other heuristics to mitigate the inevitable false positives in the extractions. All records in MOIED were randomly sampled from a database dump of magi.com in January 2020. To provide more reliable evaluation results, human annotators examined the dataset and selected 19,161 verified records for the dev and test sets. Disclaimers The dataset is expected to be used in weakly supervised scenarios since the records in the training set are not human-annotated and could be imprecise or erroneous. Records are not guaranteed to be universally correct. The correctness of extractions should be evaluated based on contexts (specified by the URLs). The extraction was made at a certain time Magi visits the URL, thus it is not guaranteed that the URL is still accessible, or the content is unmodified since the extraction was conducted. Due to legal and regulatory issues, the webpage URLs are mostly ones accessible from Mainland China, yet, the content of certain webpages, as well as the extraction results, could be in violation of law and regulation of certain countries or regions in certain ways. This dataset contains content from the Internet, for copyright reasons, please do not redistribute or use it for non-research purposes.

The dataset of morphological traits of pierid based on Fauna Sinica is extracted by using a self-developed special tool for biological morphological feature marking after digitizing the related records of the book Fauna Sinicna (INSECTA Vol. 52 Lepidoptera Pieridae). This dataset contains 24 genera, 154 species of Pierididae (177 subspecies), Lepidoptera, Insecta. Each record in this dataset is the extraction of single morphological characteristics from morphology, measurement, and other morphological description data of different species/Subspecies in different genders and various developmental stages, mainly in shape, color, and texture. This dataset provides basic data for the biological research of these pierid groups, such as biogeography and phylogeny, and also provides valuable corpus resources for natural language processing and understanding research.

Magi Entity Description Extraction Dataset (MEDED) contains 500,000 (entity, description, source URL) tuples extracted by the Magi system from Chinese webpages on the Internet that are accessible from Mainland China in May 2019. These data are learned automatically and should not be considered to contain any opinion of any human individual including the authors. Main contents of the source URLs can be found in the Magi Practical Web Article Corpus.

Enregistrement des interactions de base en chinois

This dataset includes the raw frequency counts (classical_chinese_learners_vocabularies_raw_frequencies.zip) used in the article Thoughts on ���Reliable��� Learner���s Vocabularies for Classical and Literary Chinese. Corpus I ��� Micheal Loewe (1993)���s Early Chinese Texts Corpus II ��� Official Histories (zhengshi ������) Corpus III Six Novels (xiaoshuo ������), as defined in Hsia 1968 The download includes one folder per corpus, structured as follows: xx_corpus.csv > list of texts and sources / used versions, token and type counts xx_freq_1-1.csv > unigram / character frequencies and counts xx_freq_1-4.csv > 1 to 4 character word frequencies and counts, "words" according to Hanyu da cidian ��������������� (Luo 1986���1994)) xx_freq_2-4.csv > 2 to 4 character words Additionally, pca_zhengshi_vs_loewe_vs_xiaoshuo.html is an interactive version of the Principal Component Analysis (PCA) presented in the article, texts from the three corpora are represented using the 1.000 most frequent 1���4 character combinations from the dataset. {"references": ["Schalmey, Tilman (2013). \"Corpus-driven Creation of a Reliable Learner's Vocabulary for Classical Chinese\", Taiwan huayu jiaoxue yanjiu \u81fa\u7063\u83ef\u8a9e \u6559\u5b78\u7814\u7a76 / Taiwan Journal of Chinese as a Second Language 7.2, 109\u2013137.", "Loewe, Michael, editor (1993). Early Chinese Texts: A Bibliographical Guide. The Society for the Study of Early China; The Institute of East Asian Studies, Berkeley.", "Hsia Chih-tsing (1968). The Classic Chinese Novel: A Critical Introduction. Columbia University Press, New York and London.", "Luo Zhufeng, editor (1986\u20131994). Hanyu da cidian \u6f22\u8a9e\u5927\u8a5e\u5178, volume 1-13. Cishu chubanshe, Shanghai ."]}

This video shows Wang Yanhong explaining the Dashdawa Mongol history to the representatives of five Dashdawa Mongol surname groups. He says that initially, about 1,000 Ööld people arrived at Chengde in 1757, followed by another group two years later, the same year when the Anyuan monastery in Chengde was built. Some years later, however, about 500 people were dispatched to Xinjiang to protect the Qing-Russian border areas. Sponsored by Arcadia Fund, a charitable fund of Lisbet Rausing and Peter Baldwin

This is the Speech in Cantonese and English (SpiCE) corpus. SpiCE is an audio corpus of conversational Cantonese-English bilingual speech recorded in Vancouver, Canada during 2018-2020. The corpus includes high-quality recordings of 34 early bilinguals in both English and Cantonese. Participants completed a sentence reading task, storyboard narration, and conversational interview in each language. These different speech tasks are available in a single audio file for each language for each talker. A Praat textgrid file accompanies each audio file. The textgrids provide hand-corrected orthographic transcription and phoneme-level forced-alignment in Cantonese and English. As an open-access language resource, SpiCE will promote bilingualism research for a typologically distinct pair of languages, of which Cantonese remains understudied despite there being millions of speakers around the world. The SpiCE corpus is especially well-suited for phonetic research on conversational speech, and enables researchers to study cross-language within-speaker phenomena for a diverse group of early Cantonese-English bilinguals. These are areas with few existing high-quality resources. Corpus documentation is available at: <a href="https://spice-corpus.readthedocs.io/">https://spice-corpus.readthedocs.io/</a>.

The dataset of morphological traits of birds based on Fauna Sinica is extracted by using special tools after digitizing the related records of the book Fauna Sinica (Aves 7). This dataset contains 85 species of birds belonging to 13 families of 5 orders recorded in the book, including Caprimulgiformes, Apodiformes, Trogoniformes, Coraciiformes, and Piciformes. Each record in this dataset is the extraction of single morphological characteristics from morphology, biology, distribution, measurement, and other morphological description data of different species/Subspecies in different genders and various developmental stages, mainly in shape, color, and texture. This dataset provides basic data for the biological research of these birds groups, such as biogeography and phylogeny, and also provides valuable corpus resources for natural language processing and understanding research.

[instructions for use] 1. This data set is manually edited by Yidu cloud medicine according to the real medical record distribution; 2. This dataset is an example of the yidu-n7k dataset on openkg. Yidu-n7k dataset can only be used for academic research of natural language processing, not for commercial purposes. ———————————————— Yidu-n4k data set is derived from chip 2019 evaluation task 1, that is, the data set of "clinical terminology standardization task". The standardization of clinical terms is an indispensable task in medical statistics. Clinically, there are often hundreds of different ways to write about the same diagnosis, operation, medicine, examination, test and symptoms. The problem to be solved in Standardization (normalization) is to find the corresponding standard statement for various clinical statements. With the basis of terminology standardization, researchers can carry out subsequent statistical analysis of EMR. In essence, the task of clinical terminology standardization is also a kind of semantic similarity matching task. However, due to the diversity of original word expressions, a single matching model is difficult to achieve good results. Yidu cloud, a leading medical artificial intelligence technology company in the industry, is also the first Unicorn company to drive medical innovation solutions with data intelligence. With the mission of "data intelligence and green medical care" and the goal of "improving the relationship between human beings and diseases", Yidu cloud uses data artificial intelligence to help the government, hospitals and the whole industry fully tap the intelligent political and civil value of medical big data, and build a big data ecological platform for the medical industry that can cover the whole country, make overall utilization and unified access. Since its establishment in 2013, Yidu cloud has gathered world-renowned scientists and the best people in the professional field to form a strong talent team. The company has invested hundreds of millions of yuan in R & D and service system establishment every year, built a medical data intelligent platform with large data processing capacity, high data integrity and transparent development process, and has obtained more than dozens of software copyrights and national invention patents.