长江中游地区是中部崛起的战略重点区,也是上游连接西部生态脆弱区下游连接长三角地区的生态重点区。开展长江中游战略环境影响评价需明确评价的重点区域、重点行业和重点生态环境问题。运用经济统计数据、污染物普查数据和国家、地区...长江中游地区是中部崛起的战略重点区,也是上游连接西部生态脆弱区下游连接长三角地区的生态重点区。开展长江中游战略环境影响评价需明确评价的重点区域、重点行业和重点生态环境问题。运用经济统计数据、污染物普查数据和国家、地区发展规划,采用ROST Word Parser词频分析法、ARCGIS空间分析法,分析长江中游地区战略环境影响评价重点关注区域及行业。基于SCI-EXPANDED、CPCI-S、CCR-EXPANDED、IC数据库和中国知网期刊数据库,利用Citespace文献分析法得到区域重点关注的生态环境问题。结果表明:长江中游地区战略环境影响评价的重点关注区集中分布于城镇密集区和沿江、环湖带以及周边山区;重点关注行业集中于装备制造、化工、采矿、能源、食品、纺织服装业;重点关注的生态环境问题是流域水环境和水生态、重金属污染、土壤和湿地破坏、有机物污染以及生物多样性等问题。展开更多
Long-document semantic measurement has great significance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the sema...Long-document semantic measurement has great significance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the semantic similarity of short texts. Document-level semantic measurement remains an open issue due to problems such as the omission of background knowledge and topic transition. In this paper, we propose a novel semantic matching method for long documents in the academic domain. To accurately represent the general meaning of an academic article, we construct a semantic profile in which key semantic elements such as the research purpose, methodology, and domain are included and enriched. As such, we can obtain the overall semantic similarity of two papers by computing the distance between their profiles. The distances between the concepts of two different semantic profiles are measured by word vectors. To improve the semantic representation quality of word vectors, we propose a joint word-embedding model for incorporating a domain-specific semantic relation constraint into the traditional context constraint. Our experimental results demonstrate that, in the measurement of document semantic similarity, our approach achieves substantial improvement over state-of-the-art methods, and our joint word-embedding model produces significantly better word representations than traditional word-embedding models.展开更多
文摘长江中游地区是中部崛起的战略重点区,也是上游连接西部生态脆弱区下游连接长三角地区的生态重点区。开展长江中游战略环境影响评价需明确评价的重点区域、重点行业和重点生态环境问题。运用经济统计数据、污染物普查数据和国家、地区发展规划,采用ROST Word Parser词频分析法、ARCGIS空间分析法,分析长江中游地区战略环境影响评价重点关注区域及行业。基于SCI-EXPANDED、CPCI-S、CCR-EXPANDED、IC数据库和中国知网期刊数据库,利用Citespace文献分析法得到区域重点关注的生态环境问题。结果表明:长江中游地区战略环境影响评价的重点关注区集中分布于城镇密集区和沿江、环湖带以及周边山区;重点关注行业集中于装备制造、化工、采矿、能源、食品、纺织服装业;重点关注的生态环境问题是流域水环境和水生态、重金属污染、土壤和湿地破坏、有机物污染以及生物多样性等问题。
基金supported by the Foundation of the State Key Laboratory of Software Development Environment(No.SKLSDE-2015ZX-04)
文摘Long-document semantic measurement has great significance in many applications such as semantic searchs, plagiarism detection, and automatic technical surveys. However, research efforts have mainly focused on the semantic similarity of short texts. Document-level semantic measurement remains an open issue due to problems such as the omission of background knowledge and topic transition. In this paper, we propose a novel semantic matching method for long documents in the academic domain. To accurately represent the general meaning of an academic article, we construct a semantic profile in which key semantic elements such as the research purpose, methodology, and domain are included and enriched. As such, we can obtain the overall semantic similarity of two papers by computing the distance between their profiles. The distances between the concepts of two different semantic profiles are measured by word vectors. To improve the semantic representation quality of word vectors, we propose a joint word-embedding model for incorporating a domain-specific semantic relation constraint into the traditional context constraint. Our experimental results demonstrate that, in the measurement of document semantic similarity, our approach achieves substantial improvement over state-of-the-art methods, and our joint word-embedding model produces significantly better word representations than traditional word-embedding models.
