The standard form of address is ’madam’ and it’s commonly usedwhen serving the public, for example, in a shop: Assistant: Can I help you, madam? Customer: Yes, I’d like to buy some perfume. Assistant: Certainly, m...The standard form of address is ’madam’ and it’s commonly usedwhen serving the public, for example, in a shop: Assistant: Can I help you, madam? Customer: Yes, I’d like to buy some perfume. Assistant: Certainly, madam. Did you have anything particular in mind?It’s also used in addressing a woman formally in any situation whereyou don’t know her name, and don’t plan to discover her name.展开更多
Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for man...Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for many tasks. However, the performances of the methods drop sharply when we extend the type taxonomy to a fine-grained one with several hundreds of types. In this paper, we introduce a hybrid neural network model for type classification of entity mentions with a fine-grained taxonomy. There are four components in our model, namely, the entity mention component, the context component, the relation component, the already known type component, which are used to extract features from the target entity mention, context, relations and already known types of the entity mentions in surrounding context respectively. The learned features by the four components are concatenated and fed into a softmax layer to predict the type distribution. We carried out extensive experiments to evaluate our proposed model. Experimental results demonstrate that our model achieves state-of-the-art performance on the FIGER dataset. Moreover, we extracted larger datasets from Wikipedia and DBpedia. On the larger datasets, our model achieves the comparable performance to the state-of-the-art methods with the coarse-grained type taxonomy, but performs much better than those methods with the fine-grained type taxonomy in terms of micro-F1, macro-F1 and weighted-F1.展开更多
[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook m...[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook mention on a large scale.[Methodology/Procedure]Based on three types of contextual data,including mentioned scholarly outputs,Facebook users who post scholarly outputs,and text of Facebook posts to scholarly outputs,promising relevant features were extracted,and machine learning algorithms were used to automatically identify the motivations.[Results/Conclusions](1)Features significantly correlated to the motivation of Facebook mention are identified in all three types of contextual data.In particular,relevant features are the altmetric attention score,the number of collaborative countries,the number of followers,the number of likes,the identities of Facebook users who post scholarly outputs and the number of comments on Facebook posts;(2)The prediction precision of the Light GBM classification model for motivation of Facebook mention was 0.31.In comparison,the classification precision without the text features of Facebook posts was 0.35,which was higher than the overall feature combination.The classification precision with only the post text features was 0.27.After combining the length and language of posts,the precision was improved to 0.30;(3)The classification precision of Facebook motivation has a positive correlation with users’activity.After combining all features,the classification precision of the first quartile users in terms of productivity reached 1,the classification precision of the second quartile was 0.36,and for the third quartile,the classification precision was 0.32.In conclusion,considering the high complexity of automatic classification of motivation of Facebook mentions,the study has achieved relatively high classification precision and could provide reference for future studies.展开更多
Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms...Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific domain.Current research on the mention of algorithms is limited to the academic papers in one language,which is hard to comprehensively investigate the use of algorithms.For example,in papers of Chinese conference,is the mention of algorithms consistent with it in English conference papers?In order to answer this question,this paper takes NLP as an example,and compares the mention frequency,mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference,Annual Meeting of the Association for Computational Linguistics(ACL),and the Chinese conference,China National Conference on Computational Linguistics(CCL).The results show that compared with ACL,the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed,while the distribution of mention location is similar.This study can provide a reference for the research related to the mention,citation and evaluation of knowledge entities.展开更多
You’re welcome!Not at all!Think nothing about it!Don’t mention it!意思:没关系!别见外!当别人向你表示感谢或歉意时,你可以说Don’t mention it!相同表达:You’re welcome!Not at all!Think nothing about it!
NEW quality productive forces were one of the most frequently mentioned keywords for China’s high-quality development in the past year.The annual Central Economic Work Conference(CEWC)in December that reviewed the ec...NEW quality productive forces were one of the most frequently mentioned keywords for China’s high-quality development in the past year.The annual Central Economic Work Conference(CEWC)in December that reviewed the economy in 2024 and gave directions for the economic work in 2025 called for balancing fostering new growth drivers and revitalizing old ones while developing new quality productive forces based on local conditions.展开更多
The Resolution of the Central Committee of the Communist Party of China on Further Deepening Reform Comprehensively to Advance Chinese Modernization,which was adopted at the third plenary session of the 20th Central C...The Resolution of the Central Committee of the Communist Party of China on Further Deepening Reform Comprehensively to Advance Chinese Modernization,which was adopted at the third plenary session of the 20th Central Committee of the Communist Party of China,mentioned the promotion of the debut economy.This is the first time the debut economy has been mentioned at the central level.According to the Central Economic Work Conference that was held earlier,it is imperative to promote consumption,and actively develop the debut economy,the ice and snow economy and the silver economy,clarifying the sectors where consumption is encouraged.The debut economy ranks first,owing to the high amount of attention gained from the central government.What is the debut economy and what are its major characteristics?展开更多
The aerospace and aviation industry has long been at the forefront of materials and processing technologies,driven by its ongoing demand for lightweight,highly reliable,and durable components.Precision manufacturing i...The aerospace and aviation industry has long been at the forefront of materials and processing technologies,driven by its ongoing demand for lightweight,highly reliable,and durable components.Precision manufacturing is a critical discipline that directly affects the performance,functionality,and safety of aircraft and aerospace vehicles.To meet the above-mentioned stringent requirements,advanced materials and cutting-edge processing technologies have evolved alongside aerospace innovations.展开更多
As an established environmental scholar once mentioned,“we do not inherit resources from the earth,we borrow them from the future”.Since 2011,the concentration of greenhouse gases in the atmosphere has continued to ...As an established environmental scholar once mentioned,“we do not inherit resources from the earth,we borrow them from the future”.Since 2011,the concentration of greenhouse gases in the atmosphere has continued to rise.In 2019,the annual average concentration of carbon dioxide reached 410 ppm,methane reached 1,866 ppb,and nitrous oxide reached 332 ppb.展开更多
文摘The standard form of address is ’madam’ and it’s commonly usedwhen serving the public, for example, in a shop: Assistant: Can I help you, madam? Customer: Yes, I’d like to buy some perfume. Assistant: Certainly, madam. Did you have anything particular in mind?It’s also used in addressing a woman formally in any situation whereyou don’t know her name, and don’t plan to discover her name.
文摘Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for many tasks. However, the performances of the methods drop sharply when we extend the type taxonomy to a fine-grained one with several hundreds of types. In this paper, we introduce a hybrid neural network model for type classification of entity mentions with a fine-grained taxonomy. There are four components in our model, namely, the entity mention component, the context component, the relation component, the already known type component, which are used to extract features from the target entity mention, context, relations and already known types of the entity mentions in surrounding context respectively. The learned features by the four components are concatenated and fed into a softmax layer to predict the type distribution. We carried out extensive experiments to evaluate our proposed model. Experimental results demonstrate that our model achieves state-of-the-art performance on the FIGER dataset. Moreover, we extracted larger datasets from Wikipedia and DBpedia. On the larger datasets, our model achieves the comparable performance to the state-of-the-art methods with the coarse-grained type taxonomy, but performs much better than those methods with the fine-grained type taxonomy in terms of micro-F1, macro-F1 and weighted-F1.
基金supported by Hum anity and Social Science Foundation of Ministry of Education of China(22YJA870016)National Natural Science Foundation of China(NO.72274227)
文摘[Purpose/Significance]The article investigated the automatic identification of the motivation of Facebook mention to scholarly outputs based on Light GBM algorithm,in order to achieve more in-depth usage of Facebook mention on a large scale.[Methodology/Procedure]Based on three types of contextual data,including mentioned scholarly outputs,Facebook users who post scholarly outputs,and text of Facebook posts to scholarly outputs,promising relevant features were extracted,and machine learning algorithms were used to automatically identify the motivations.[Results/Conclusions](1)Features significantly correlated to the motivation of Facebook mention are identified in all three types of contextual data.In particular,relevant features are the altmetric attention score,the number of collaborative countries,the number of followers,the number of likes,the identities of Facebook users who post scholarly outputs and the number of comments on Facebook posts;(2)The prediction precision of the Light GBM classification model for motivation of Facebook mention was 0.31.In comparison,the classification precision without the text features of Facebook posts was 0.35,which was higher than the overall feature combination.The classification precision with only the post text features was 0.27.After combining the length and language of posts,the precision was improved to 0.30;(3)The classification precision of Facebook motivation has a positive correlation with users’activity.After combining all features,the classification precision of the first quartile users in terms of productivity reached 1,the classification precision of the second quartile was 0.36,and for the third quartile,the classification precision was 0.32.In conclusion,considering the high complexity of automatic classification of motivation of Facebook mentions,the study has achieved relatively high classification precision and could provide reference for future studies.
基金supported by the National Natural Science Foundation of China(Grant No.72074113)
文摘Algorithms play an increasingly important role in scientific work,especially in data-driven research.Investigating the mention of algorithms in full-text paper helps us understand the use and development of algorithms in a specific domain.Current research on the mention of algorithms is limited to the academic papers in one language,which is hard to comprehensively investigate the use of algorithms.For example,in papers of Chinese conference,is the mention of algorithms consistent with it in English conference papers?In order to answer this question,this paper takes NLP as an example,and compares the mention frequency,mention location and mention time of the top10 data-mining algorithms between the papers of the famous international conference,Annual Meeting of the Association for Computational Linguistics(ACL),and the Chinese conference,China National Conference on Computational Linguistics(CCL).The results show that compared with ACL,the mention frequency of top10 data-mining algorithms in CCL is slightly lower and the mention time is slightly delayed,while the distribution of mention location is similar.This study can provide a reference for the research related to the mention,citation and evaluation of knowledge entities.
文摘You’re welcome!Not at all!Think nothing about it!Don’t mention it!意思:没关系!别见外!当别人向你表示感谢或歉意时,你可以说Don’t mention it!相同表达:You’re welcome!Not at all!Think nothing about it!
文摘NEW quality productive forces were one of the most frequently mentioned keywords for China’s high-quality development in the past year.The annual Central Economic Work Conference(CEWC)in December that reviewed the economy in 2024 and gave directions for the economic work in 2025 called for balancing fostering new growth drivers and revitalizing old ones while developing new quality productive forces based on local conditions.
文摘The Resolution of the Central Committee of the Communist Party of China on Further Deepening Reform Comprehensively to Advance Chinese Modernization,which was adopted at the third plenary session of the 20th Central Committee of the Communist Party of China,mentioned the promotion of the debut economy.This is the first time the debut economy has been mentioned at the central level.According to the Central Economic Work Conference that was held earlier,it is imperative to promote consumption,and actively develop the debut economy,the ice and snow economy and the silver economy,clarifying the sectors where consumption is encouraged.The debut economy ranks first,owing to the high amount of attention gained from the central government.What is the debut economy and what are its major characteristics?
文摘The aerospace and aviation industry has long been at the forefront of materials and processing technologies,driven by its ongoing demand for lightweight,highly reliable,and durable components.Precision manufacturing is a critical discipline that directly affects the performance,functionality,and safety of aircraft and aerospace vehicles.To meet the above-mentioned stringent requirements,advanced materials and cutting-edge processing technologies have evolved alongside aerospace innovations.
文摘As an established environmental scholar once mentioned,“we do not inherit resources from the earth,we borrow them from the future”.Since 2011,the concentration of greenhouse gases in the atmosphere has continued to rise.In 2019,the annual average concentration of carbon dioxide reached 410 ppm,methane reached 1,866 ppb,and nitrous oxide reached 332 ppb.
