Script is the structured knowledge representation of prototypical real-life event sequences.Learning the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawin...Script is the structured knowledge representation of prototypical real-life event sequences.Learning the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible inferences.Script learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw inferences.However,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script learning.This research field contains three main topics:event representations,script learning models,and evaluation approaches.For each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative systems.We also discuss the current state of the research and possible future directions.展开更多
采用客户机/服务器网络体系结构,选取Microsoft SQL Server 2000作为数据库管理系统,在Sybase Power-Builder平台下,运用PowerScript语言开发了焊工考试题库管理系统。系统包含试题编辑,试卷管理,题库设置和图片编辑等四大功能,实现了...采用客户机/服务器网络体系结构,选取Microsoft SQL Server 2000作为数据库管理系统,在Sybase Power-Builder平台下,运用PowerScript语言开发了焊工考试题库管理系统。系统包含试题编辑,试卷管理,题库设置和图片编辑等四大功能,实现了网络化焊工题库的管理。系统以填空题、判断题、选择题、简答题、计算题的形式集成5000多道焊接考试标准化试题,内容涉及焊接基础、焊接冶金、焊接材料、焊接方法及焊接设备、金属材料及其焊接、焊接应力与变形、焊接缺陷及焊接检验、焊接与切割安全技术等方面,可作为焊工培训、焊工及焊接技师理论知识考试及各级焊接技术比赛的题库系统。展开更多
We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. S...We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. Such sentences are ambiguous with respect to the number of trees inferred;either several trees were climbed or just one. The availability of the NOUN VERB NOUN (N-V-N) heuristic, e.g., KID CLIMB TREE, should contribute to the interpretation of how many trees were climbed. Specifically, we hypothesized that number choices for these stimuli would be predicted by choices previously made to corresponding (full) sentences. 45 participants were instructed to treat N-V-N triplets such as KID CLIMB TREE as telegrams and select a picture, regarding the quantity (“several” vs. “one”) associated with tree. Results confirmed that plural responses to quantifier scope ambiguous sentences significantly predict increased plural judgments in the picture-matching task. This result provides empirical evidence that the N-V-N heuristic, via conceptual event knowledge, can influence sentence interpretation. Furthermore, event knowledge must include the quantity of participants in the event (especially in terms of “several” vs. “one”). These findings are consistent with our model of language comprehension functioning as “Heuristic first, algorithmic second.” Furthermore, results are consistent with judgment and decision making in other cognitive domains.展开更多
基金Project supported by the National Natural Science Foundation of China(No.61806216)。
文摘Script is the structured knowledge representation of prototypical real-life event sequences.Learning the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible inferences.Script learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw inferences.However,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script learning.This research field contains three main topics:event representations,script learning models,and evaluation approaches.For each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative systems.We also discuss the current state of the research and possible future directions.
文摘采用客户机/服务器网络体系结构,选取Microsoft SQL Server 2000作为数据库管理系统,在Sybase Power-Builder平台下,运用PowerScript语言开发了焊工考试题库管理系统。系统包含试题编辑,试卷管理,题库设置和图片编辑等四大功能,实现了网络化焊工题库的管理。系统以填空题、判断题、选择题、简答题、计算题的形式集成5000多道焊接考试标准化试题,内容涉及焊接基础、焊接冶金、焊接材料、焊接方法及焊接设备、金属材料及其焊接、焊接应力与变形、焊接缺陷及焊接检验、焊接与切割安全技术等方面,可作为焊工培训、焊工及焊接技师理论知识考试及各级焊接技术比赛的题库系统。
文摘We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. Such sentences are ambiguous with respect to the number of trees inferred;either several trees were climbed or just one. The availability of the NOUN VERB NOUN (N-V-N) heuristic, e.g., KID CLIMB TREE, should contribute to the interpretation of how many trees were climbed. Specifically, we hypothesized that number choices for these stimuli would be predicted by choices previously made to corresponding (full) sentences. 45 participants were instructed to treat N-V-N triplets such as KID CLIMB TREE as telegrams and select a picture, regarding the quantity (“several” vs. “one”) associated with tree. Results confirmed that plural responses to quantifier scope ambiguous sentences significantly predict increased plural judgments in the picture-matching task. This result provides empirical evidence that the N-V-N heuristic, via conceptual event knowledge, can influence sentence interpretation. Furthermore, event knowledge must include the quantity of participants in the event (especially in terms of “several” vs. “one”). These findings are consistent with our model of language comprehension functioning as “Heuristic first, algorithmic second.” Furthermore, results are consistent with judgment and decision making in other cognitive domains.
