权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

III: Small: Using Location for Retrieving Text and Images in News And Social Media Posts

III：小：使用位置检索新闻和社交媒体帖子中的文本和图像

基本信息

批准号：
1816889
负责人：
Hanan Samet
金额：
$ 50万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816889&HistoricalAwards=false
关键词：
III Small Using Location Retrieving

项目摘要

The goal of this project is to enhance systems which make use of a map interface to access documents such as news and social media postings. Most geographic search systems require the user to enter the latitude and longitude of two corners of the bounding box that encloses the region of interest. Some allow locations to be specified by name, but the region associated with the name is fixed in advance and is often different from the region that the user intends to search. For example, a user might enter the location name "Los Angeles", hoping the search for social media posts from the city. However, the system might be designed to interpret "Los Angeles" to mean the greater Los Angeles area (too large) or the geographic point center of the city (too small). This project will create a system that overcomes these problems with text geographic queries. The underlying textual data is accessed via a map query interface using direct manipulation actions such as pan and zoom to navigate the data. The advantage of these actions is that the act of pointing at a location (e.g., by the appropriate positioning of a pointing device) and making the interpretation of the precision of this positioning specification dependent on the zoom level is equivalent to permitting spatial synonyms. This means that when searching for a "rock concert in Manhattan", responses such as Harlem, New York City, and Brooklyn are all acceptable when no concert is found in Harlem. The search for Harlem is expanded by using its spatial synonyms since Harlem is contained in Manhattan, New York City includes Manhattan, and Brooklyn is a sibling (neighbor) of Manhattan. The location-oriented map-based provides a new paradigm to reading and analyzing news and social media on a local level or global level. This project addresses challenges in: (1) Detecting tweets or other social media posts about local events. This is difficult as only a few people may be posting related tweets in contrast to global events where many people post tweets thereby making it easier to detect global events. (2) Improving the resolution of ambiguous location names when retrieving documents using textually-specified locations by developing more appropriate precision and recall evaluation metrics. (3) Enabling domain-specific tracking of mentions of events such as crimes and diseases in news and social media such as Twitter over time with the aid of heat maps which may have an impact on public safety and health. (4) Allowing users to specify the desired domain as well as infer it by use of exemplars. (5) Improving the method the NewsStand system, a prior contribution of the investigator, uses clustering by using word2vec which makes better use of semantics than the currently used TF-IDF. This clustering is used for the actual documents and their associated images and videos, and enables the detection of similar images based on semantics rather than local features such as color and texture.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的目标是加强利用地图界面获取新闻和社交媒体帖子等文件的系统。大多数地理搜索系统要求用户输入包围感兴趣区域的边界框的两个角的纬度和经度。有些允许通过名称指定位置，但与名称关联的区域是预先固定的，并且通常与用户想要搜索的区域不同。例如，用户可能输入位置名称“洛杉矶”，希望搜索来自该城市的社交媒体帖子。然而，该系统可能被设计成将“洛杉矶”解释为大洛杉矶地区(太大)或城市的地理点中心(太小)。该项目将创建一个系统，克服文本地理查询的这些问题。底层文本数据通过地图查询界面使用直接操作操作(如平移和缩放)来导航数据。这些动作的优点在于，指向某一位置(例如，通过指向设备的适当定位)并使该定位规范的精度的解释依赖于缩放级别的动作等同于允许空间同义词。这就是说，在搜索一场“曼哈顿摇滚音乐会”时，如果没有在哈莱姆找到演唱会，哈莱姆、纽约市和布鲁克林等地的搜索结果都是可以接受的。由于哈莱姆包含在曼哈顿，纽约市包括曼哈顿，而布鲁克林是曼哈顿的兄弟(邻居)，因此通过使用空间同义词扩大了对哈莱姆的搜索。面向位置的地图为阅读和分析地方或全球层面的新闻和社交媒体提供了新的范式。该项目解决了以下方面的挑战：(1)检测关于当地事件的推文或其他社交媒体帖子。这很困难，因为可能只有几个人发布相关的推文，而全球事件则是许多人发布推文，从而更容易发现全球事件。(2)通过开发更合适的查准率和召回率评估指标，提高使用文本指定位置检索文档时位置名称歧义的分辨率。(3)借助可能对公共安全和健康产生影响的热图，能够针对特定领域跟踪在推特等新闻和社交媒体中提及的事件，如犯罪和疾病。(4)允许用户指定所需的领域，并使用样本进行推理。(5)改进方法报摊系统采用了基于word2vec的分簇方法，比目前使用的tf-idf算法更好地利用了语义。此聚类用于实际文档及其相关图像和视频，并能够基于语义而不是颜色和纹理等本地特征检测相似图像。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Data Management and Analytics System for Online Flight Conformance Monitoring and Anomaly Detection

DOI：
10.1145/3347146.3359378
发表时间：
2019-11
期刊：
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
影响因子：
0
作者：
S. Ayhan;H. Samet
通讯作者：
S. Ayhan;H. Samet

Enhancing local live tweet stream to detect news

DOI：
10.1007/s10707-019-00392-9
发表时间：
2018-11
期刊：
GeoInformatica
影响因子：
2
作者：
Hong Wei;Jagan Sankaranarayanan;H. Samet
通讯作者：
Hong Wei;Jagan Sankaranarayanan;H. Samet

Querying Recurrent Convoys over Trajectory Data

DOI：
10.1145/3400730
发表时间：
2020-08
期刊：
ACM Transactions on Intelligent Systems and Technology (TIST)
影响因子：
0
作者：
Munkh-Erdene Yadamjav;Z. Bao;Baihua Zheng;F. Choudhury;H. Samet
通讯作者：
Munkh-Erdene Yadamjav;Z. Bao;Baihua Zheng;F. Choudhury;H. Samet

DOS: a spatial system offering extremely high-throughput road distance computations

DOI：
10.1145/3274895.3274898
发表时间：
2018-11
期刊：
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
影响因子：
0
作者：
Shangfu Peng;Jagan Sankaranarayanan;H. Samet
通讯作者：
Shangfu Peng;Jagan Sankaranarayanan;H. Samet

An efficient region expansion algorithm for regular triangulated meshes

一种有效的规则三角网格区域扩展算法