A systematic review and comparative analysis of cross-document coreference resolution methods and tools

Beheshti, Amin; Benatallah, Boualem; Venugopal, Srikumar; Ryu, Seung Hwan; Motahari-Nezhad, Hamid Reza; Wang, Wei

doi:10.1007/s00607-016-0490-0

Cited by 42 publications

(25 citation statements)

References 49 publications

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“…In this context, data curation [144]- [146] (i.e., the task of preparing the raw data for analytics) can help in turning raw data into contextualised data and knowledge. For example, curating a raw tweet from Twitter can tell us if the tweet contains a mention of a person named Barak Obama (using entity extraction and coreference reolution techniques [147]) who was the 44th president of the United States (using linking techniques [148] to link this entity to external knowledge sources such as Wikidata 7 ).…”

Section: Approach Time Complexitymentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

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Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

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Section: Approach Time Complexitymentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

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“…Moreover, Singh, Wick, and McCallum (2010) proposed a discriminative model which is trained on a distantly labeled data set generated from Wikipedia. A recent review of the CR literature is provided by Beheshti et al (2017). CR has also been used in a joint task with entity linking (Monahan et al 2011;Dutta and Weikum 2015).…”

Section: Related Workmentioning

confidence: 99%

Cluster-based mention typing for named entity disambiguation

Çelebi

Özgür

2020

Nat. Lang. Eng.

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An entity mention in text such as “Washington” may correspond to many different named entities such as the city “Washington D.C.” or the newspaper “Washington Post.” The goal of named entity disambiguation (NED) is to identify the mentioned named entity correctly among all possible candidates. If the type (e.g., location or person) of a mentioned entity can be correctly predicted from the context, it may increase the chance of selecting the right candidate by assigning low probability to the unlikely ones. This paper proposes cluster-based mention typing for NED. The aim of mention typing is to predict the type of a given mention based on its context. Generally, manually curated type taxonomies such as Wikipedia categories are used. We introduce cluster-based mention typing, where named entities are clustered based on their contextual similarities and the cluster ids are assigned as types. The hyperlinked mentions and their context in Wikipedia are used in order to obtain these cluster-based types. Then, mention typing models are trained on these mentions, which have been labeled with their cluster-based types through distant supervision. At the NED phase, first the cluster-based types of a given mention are predicted and then, these types are used as features in a ranking model to select the best entity among the candidates. We represent entities at multiple contextual levels and obtain different clusterings (and thus typing models) based on each level. As each clustering breaks the entity space differently, mention typing based on each clustering discriminates the mention differently. When predictions from all typing models are used together, our system achieves better or comparable results based on randomization tests with respect to the state-of-the-art levels on four defacto test sets.

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“…Hameed et al provided a way to classify the recommended systems by calculating similarity (Kumar et al, 2017). Other scholars have also conducted research on keyword similarity matching models (Hameed et al, 2012Beheshti et al, 2017Zouaq et al, 2017;Ding et al, 2016). Among them, Kumar et al (2017) proposed a weighted semantic information extraction algorithm idea by extracting nouns and verbs from the marker data, then providing different possibilities for each noun or verb to extract semantic-based terms; next these weights were established using cosine similarity.…”

Section: Semantic Extraction Of Keyword Matching and Similarity Retrimentioning

confidence: 99%

A decision system based on active perception and intelligent analysis for key location security information

Chen

Cao

et al. 2019

IJCSE

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In various enterprises, the security data are manually entered by multiple security workers at different times, which can lead to disordered data in the system, and also, analysis decision files need to be manually processed. To solve these problems, this paper presents a decision system for key location security information based on active perception and intelligent analysis. First, the designed system is developed based on C/S framework. Then, a deep learning perception model is constructed. Experimental results show that this system can significantly reduce the heavy workload of security inspectors, and reduce the frequency of safety accidents.

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A systematic review and comparative analysis of cross-document coreference resolution methods and tools

Cited by 42 publications

References 49 publications

A Survey on Trust Prediction in Online Social Networks

A Survey on Trust Prediction in Online Social Networks

Cluster-based mention typing for named entity disambiguation

A decision system based on active perception and intelligent analysis for key location security information

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