Using semantic similarity to reduce wrong labels in distant supervision for relation extraction     

Chengsen Ru  Jintao Tang  Shasha Li  Songxian Xie  Ting Wang 《Information processing & management》2018,54(4):593-608

Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms.  相似文献   

网络招聘文本技能信息自动抽取研究   总被引：1，自引：1，他引：0  

俞琰  陈磊  姜金德  赵乃瑄 《图书情报工作》2019,63(13):105-113

[目的/意义]针对目前网络招聘文本手工抽取技能信息无法满足大数据量分析要求的问题，提出一种针对大量网络招聘文本的技能信息自动抽取方法。[方法/过程]根据网络招聘文本的特点，利用依存句法分析选取候选技能，然后提出领域相关性指标衡量候选技能，将其融入传统的术语抽取方法之中，形成一种网络招聘文本技能信息自动抽取方法。[结果/结论]实验表明，本文提出的方法能够从网络招聘文本中自动、快速、准确地抽取技能信息。  相似文献   

Self-training on refined clause patterns for relation extraction     

Duc-Thuan Vo  Ebrahim Bagheri 《Information processing & management》2018,54(4):686-706

Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques.  相似文献   

Local word vectors guiding keyphrase extraction     

Eirini Papagiannopoulou  Grigorios Tsoumakas 《Information processing & management》2018,54(6):888-902

Automated keyphrase extraction is a fundamental textual information processing task concerned with the selection of representative phrases from a document that summarize its content. This work presents a novel unsupervised method for keyphrase extraction, whose main innovation is the use of local word embeddings (in particular GloVe vectors), i.e., embeddings trained from the single document under consideration. We argue that such local representation of words and keyphrases are able to accurately capture their semantics in the context of the document they are part of, and therefore can help in improving keyphrase extraction quality. Empirical results offer evidence that indeed local representations lead to better keyphrase extraction results compared to both embeddings trained on very large third corpora or larger corpora consisting of several documents of the same scientific field and to other state-of-the-art unsupervised keyphrase extraction methods.  相似文献   

基于动态分阶势场法的船舶自动避碰系统     

罗贤程  尹建川  李宏宾 《上海海事大学学报》2020,41(1):1-7

为使船舶在多船避让环境下适应航行环境的变化并自动复航,提出基于动态分阶势场法的船舶自动避碰系统。该系统基于动态势场避障规划算法,利用模糊综合评价法量化船舶碰撞危险度;依据《国际海上避碰规则》确定本船在不同会遇局面下的避让行动,并据此对斥力势函数进行调整。该系统将本船的避碰过程划分为航迹保持、避让和复航3个阶段,根据不同阶段的要求构建动态分阶势场;利用动态分阶势场法对船舶的航行环境进行建模,进而生成恰当的航向指令;利用自动舵产生舵角指令控制船舶完成避让、复航和航迹保持。仿真结果表明:该自动避碰系统可以引导船舶完成既定的避让行动,并能使船舶在安全会遇距离上驶过;该系统可以适应航行环境的变化,且具备航迹保持能力。  相似文献   

基于无人机激光点云的树障检测与砍伐树木数量估算     

张苏  齐立忠  韩文军  朱笑笑  习晓环  王成  王濮  聂胜 《中国科学院大学学报》2020,37(6):760-766

电力走廊的树木生长会对输电线路的安全运营造成巨大影响，精确检测出影响线路安全的树木并将其砍伐至关重要。因此，提出一种基于无人机激光点云的树障检测与砍伐树木数量估算方法。首先，对激光点云进行快速自动化处理，先后精确提取地面点、电力线点与植被点；其次，基于电力线点进行分段，并分析电力线与植被点的安全距离，进而确定树障区域的位置和范围；最后，对树障区域植被点云进行单木分割，并统计单木数量，最终实现砍伐树木数量的精准估算。研究结果表明，无人机激光点云可以实现输电通道树障的有效检测与砍伐树木数量的精确估算，总体树木砍伐数量估算精度可达92.3%，可为输电线路安全运营提供遥感技术支撑，也可为电网运维单位制订树木砍伐计划提供可靠依据。  相似文献   

Combined effects of exposure to hypoxia and cool on walking economy and muscle oxygenation profiles at tibialis anterior     

Masahiro Horiuchi  Yoko Handa-Kirihara  Daijiro Abe  Yoshiyuki Fukuoka 《Journal of sports sciences》2019,37(14):1638-1647

We investigated combined effects of ambient temperature (23°C or 13°C) and fraction of inspired oxygen (21%O₂ or 13%O₂) on energy cost of walking (C_w: J·kg^?1·km^?1) and economical speed (ES). Eighteen healthy young adults (11 males, seven females) walked at seven speeds from 0.67 to 1.67 m s^?1 (four min per stage). Environmental conditions were set; thermoneutral (N: 23°C) with normoxia (N: 21%O₂) = NN; 23°C (N) with hypoxia (H: 13%O₂) = NH; cool (C: 13°C) with 21%O₂ (N) = CN, and 13°C (C) with 13%O₂ (H) = CH. Muscle deoxygenation (HHb) and tissue O₂ saturation (StO₂) were measured at tibialis anterior. We found a significantly slower ES in NH (1.289 ± 0.091 m s^?1) and CH (1.275 ± 0.099 m s^?1) than in NN (1.334 ± 0.112 m s^?1) and CN (1.332 ± 0.104 m s^?1). Changes in HHb and StO₂ were related to the ES. These results suggested that the combined effects (exposure to hypoxia and cool) is nearly equal to exposure to hypoxia and cool individually. Specifically, acute moderate hypoxia slowed the ES by approx. 4%, but acute cool environment did not affect the ES. Further, HHb and StO₂ may partly account for an individual ES.  相似文献   

基于Sentinel-1/2的水体提取方法对比研究——以斯里兰卡小型水体为例     

周晗  叶虎平  魏显虎  张宗科  王法溧  齐永菊  马建如 《中国科学院大学学报》2019,36(6):794-802

地表水面精确提取是研究地表水质和水量变化的重要基础。斯里兰卡是"21世纪海上丝绸之路"的重要参与国，年降雨量丰富，但时空分布不均，斯里兰卡人民长期用水困难，研究斯里兰卡地表水体有助于斯里兰卡民生问题的解决。斯里兰卡国内散布着大量的小型水库和坑塘，这些小面积水体易受周边环境因素影响而提取困难。基于2017年7月斯里兰卡中东部地区的哨兵（Sentinel）1/2号卫星影像，对比分析单波段法、水体指数法和监督分类等水体提取方法的精度和存在的问题。结果表明，归一化水体指数法NDWI的准确率最高，分类精度达94%。  相似文献   

基于深度学习的中文专利自动分类方法研究   总被引：2，自引：0，他引：2  

吕璐成  韩涛  周健  赵亚娟 《图书情报工作》2020,64(10):75-85

[目的/意义] 面向当前国内专利审查和专利情报分析工作中对于海量专利分类的客观需求，设计了7种基于深度学习的专利自动分类方法，对比各种方法的分类效果，从而助力专利分类效率和效果的提升。[方法/过程] 针对传统机器学习方法存在的缺陷，基于Word2Vec、CNN、RNN、Attention机制等深度学习技术，考虑专利文本语序特征、上下文特征以及分类关键特征，设计Word2Vec+TextCNN、Word2Vec+GRU、Word2Vec+BiGRU、Word2Vec+BiGRU+TextCNN等7种深度学习模型，以中国专利为例，选取IPC主分类号的"部"作为分类依据，对比这7种模型与3种传统分类模型在中文专利分类任务中的效果。[结果/结论] 实证研究效果显示，采用考虑语序特征、上下文特征及强化关键特征的深度学习方法进行中文专利分类具有更优的分类效果。  相似文献   

Location extraction from tweets     

Thi Bich Ngoc Hoang  Josiane Mothe 《Information processing & management》2018,54(2):129-144

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.  相似文献