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1.
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   
2.
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  
[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   
3.
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   
4.
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   
5.
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   
6.
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 (Cw: 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   
7.
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   
8.
Handwriter identification aims to simplify the task of forensic experts by providing them with semi-automated tools in order to enable them to narrow down the search to determine the final identification of an unknown handwritten sample. An identification algorithm aims to produce a list of predicted writers of the unknown handwritten sample ranked in terms of confidence measure metrics for use by the forensic expert will make the final decision.Most existing handwriter identification systems use either statistical or model-based approaches. To further improve the performances this paper proposes to deploy a combination of both approaches using Oriented Basic Image features and the concept of graphemes codebook. To reduce the resulting high dimensionality of the feature vector a Kernel Principal Component Analysis has been used. To gauge the effectiveness of the proposed method a performance analysis, using IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting, has been carried out. The results obtained achieved an accuracy of 96% thus demonstrating its superiority when compared against similar techniques.  相似文献   
9.
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   
10.
This study explored third-graders’ oral reading fluency (ORF) in easy text in relation to their third- and fourth-grade reading comprehension. It also examined the children’s performance on two different measures of text exposure, a self-report questionnaire and a title-recognition test. Although third-graders’ ORF related significantly to their reading comprehension, oral language comprehension accounted for most of the variance in reading comprehension, whereas single word reading speed accounted for most of the variance in ORF. Third-grade reading comprehension and ORF each predicted unique variance in children’s scores on a fourth-grade state-mandated reading comprehension assessment. Scores on the self-report questionnaire correlated significantly with third-grade ORF and fourth-grade reading; the self-report accounted for reliable variance in ORF even with all of the other reading ability variables entered first. Results are consistent with the viewpoint that text exposure affects reading fluency. They also demonstrate that ORF is a valuable predictor of middle-elementary children’s reading comprehension, even when the ORF measure employs very easy text in which children achieve near-perfect word accuracy.  相似文献   
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