Acquisition of instance attributes via labeled and related instances

Alfonseca, Enrique; Paşca, Marius; Robledo-Arnuncio, Enrique

doi:10.1145/1835449.1835462

Cited by 27 publications

(16 citation statements)

References 18 publications

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“…For instance, Paşca [28] mined instances of semantic classes from query logs using information extraction and Alfonseca et al [1] mined query logs to find attributes of entity instances. Once equipped with a reliable entity linking system, it is possible to provide deeper query analyses of user patterns and web usage [15].…”

Section: Related Workmentioning

confidence: 99%

Fast and Space-Efficient Entity Linking for Queries

Blanco

Ottaviano

Meij

2015

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

155

108

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Entity linking deals with identifying entities from a knowledge base in a given piece of text and has become a fundamental building block for web search engines, enabling numerous downstream improvements from better document ranking to enhanced search results pages. A key problem in the context of web search queries is that this process needs to run under severe time constraints as it has to be performed before any actual retrieval takes place, typically within milliseconds.In this paper we propose a probabilistic model that leverages user-generated information on the web to link queries to entities in a knowledge base. There are three key ingredients that make the algorithm fast and space-efficient. First, the linking process ignores any dependencies between the different entity candidates, which allows for a O(k 2 ) implementation in the number of query terms. Second, we leverage hashing and compression techniques to reduce the memory footprint. Finally, to equip the algorithm with contextual knowledge without sacrificing speed, we factor the distance between distributional semantics of the query words and entities into the model.We show that our solution significantly outperforms several state-of-the-art baselines by more than 14% while being able to process queries in sub-millisecond times-at least two orders of magnitude faster than existing systems.

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Section: Related Workmentioning

confidence: 99%

Fast and Space-Efficient Entity Linking for Queries

Blanco

Ottaviano

Meij

2015

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

155

108

View full text Add to dashboard Cite

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“…By learning distinguishing attributes of certain classes of users through third-person text, [5] aims to classify users in the analysis of first-person communication. The attribute extraction method is based on [2]. Using the networks and cities of US LiveJournal members, [15] finds that the likelihood of friendship is almost inversely proportional to distance of location.…”

Section: Inferring User Attributesmentioning

confidence: 99%

A study of age gaps between online friends

Liao

Jiang

Lim

et al. 2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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User attribute extraction on social media has gain considerable attention, while existing methods are mostly supervised which suffer great difficulty in insufficient gold standard data. In this paper, we validate a strong hypothesis based on homophily and adapt it to ensure the certainty of user attribute we extracted via weakly supervised propagation. Homophily, the theory which states that people who are similar tend to become friends, has been well studied in the setting of online social networks. When we focus on age attribute, based on this theory, online friends tend to have similar age. In this work, we take a step further and study the hypothesis that the age gap between online friends become even smaller in a larger friendship clique. We empirically validate our hypothesis using two real social network data sets. We further design a propagation-based algorithm to predict online users' age, leveraging the clique-based hypothesis. We find that our algorithm can outperform several baselines. We believe that this method could work as a way to enrich sparse data and the hypothesis we validated would shed light on exploring the proximity of other user attributes such as education as well.

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“…They usually used lexico-syntactic templates for the extraction. Only a few researchers started to use similarity as a tool for attribute retrieval, such as Alfonseca et al (2010) and Liu and Duan (2015).…”

Section: Attribute Mining Approachesmentioning

confidence: 99%

Geometric analysis of concept vectors based on similarity values

Liu

Duan

2017

lingua. sin.

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In this paper, we offer a geometric framework for the computing of a concept's conceptual vector based on its similarity position with other concepts in a vector space called concept space, which is a set of concept vectors together with a distance function derived from a similarity model. We show that there exists an isometry to map a concept space to a Euclidean space. So, the concept vector can be mapped to a coordinate in a Euclidean space and vice versa. Therefore, given only the similarity position of a concept, we can locate its coordinate and its concept vector subsequently, using distance geometry methods. We prove that such mapping functions do exist under some conditions. We also discuss how to map non-numerical attributes. At last, we show some preliminary experimental results and thoughts in the implementation of an attribute mining task. This work will benefit attribute retrieval tasks.

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Acquisition of instance attributes via labeled and related instances

Cited by 27 publications

References 18 publications

Fast and Space-Efficient Entity Linking for Queries

Fast and Space-Efficient Entity Linking for Queries

A study of age gaps between online friends

Geometric analysis of concept vectors based on similarity values

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