Contextual Fact Ranking and Its Applications in Table Synthesis and Compression

Huang, Silu; Liu, Jialu; Korn, Flip; Wang, Xuezhi; Wu, You; Markowitz, Dale; Yu, Cong

doi:10.1145/3292500.3330980

Cited by 5 publications

(4 citation statements)

References 28 publications

(30 reference statements)

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“…As pointed out by [23], most of the real world table synthesis or compression applications often have the number of relevant facts about entities (columns of the output table) set to 3, so that the rendered table can fit on the phone screen. This observation makes us believe that an effective solution for a smaller subset of columns can help generalize our approach to the majority of the daily use cases.…”

Section: Problem Statementmentioning

confidence: 99%

“…: [35,49,50,53]). Recently, search engines (like Google) also tend to return tables as answer to list-seeking user queries [4,23] thereby making tabular representation even more popular.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, either extra information is retrieved possibly overwhelming the user with additional unwanted columns, or some information is missed by not gathering columns of interest. Recent works that try to automatically generate/predict the relevant columns [23,55] of the output table, might be well suited for a rather exploratory information gathering task. However, tabular queries enable the user, who has a well-defined information requirement, to specify the general topic as well as the structure of the desired table, thereby allowing more granular control on the output, when compared to traditional natural language queries.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Table completion using Knowledge Base

Bandyopadhyay,

Deng,

Bajaj

et al. 2019

Preprint

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Table is a popular data format to organize and present relational information. Users often have to manually compose tables when gathering their desiderate information (e.g., entities and their attributes) for decision making. In this work, we propose to resolve a new type of heterogeneous query viz: tabular query, which contains a natural language query description, column names of the desired table, and an example row. We aim to acquire more entity tuples (rows) and automatically fill the table specified by the tabular query. We design a novel framework AutoTableComplete which aims to integrate schema specific structural information with the natural language contextual information provided by the user, to complete tables automatically, using a heterogeneous knowledge base (KB) as the main information source. Given a tabular query as input, our framework first constructs a set of candidate chains that connect the given example entities in KB. We learn to select the best matching chain from these candidates using the semantic context from tabular query. The selected chain is then converted into a SPARQL query, executed against KB to gather a set of candidate rows, that are then ranked in order of their relevance to the tabular query, to complete the desired table. We construct a new dataset based on tables in Wikipedia pages and Freebase, using which we perform a wide range of experiments to demonstrate the effectiveness of AutoTableComplete as well as present a detailed error analysis of our method.

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Section: Problem Statementmentioning

confidence: 99%

“…: [35,49,50,53]). Recently, search engines (like Google) also tend to return tables as answer to list-seeking user queries [4,23] thereby making tabular representation even more popular.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Table completion using Knowledge Base

Bandyopadhyay,

Deng,

Bajaj

et al. 2019

Preprint

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show abstract

“…Concept graphs are created as universal taxonomies for text understanding and reasoning in the open domain knowledge (Dagan et al, 2010;Bowman et al, 2015;Zamir et al, 2018;Huang et al, 2019;Hao et al, 2019;Jiang et al, 2019). The nodes in concept graphs include both entities and concepts.…”

Section: Introductionmentioning

confidence: 99%

Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts

Zhang

Sun

Feng

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both English and Chinese datasets.

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Carousel Personalization in Music Streaming Apps with Contextual Bandits

Bendada¹,

Salha²,

Bontempelli³

2020

Fourteenth ACM Conference on Recommender Systems

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Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most relevant items (albums, artists, playlists...) to display in these carousels is a challenging task, as items are numerous and as users have different preferences. In this paper, we model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app. Along with this paper, we publicly release industrial data from our experiments, as well as an open-source environment to simulate comparable carousel personalization learning problems.

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Contextual Fact Ranking and Its Applications in Table Synthesis and Compression

Cited by 5 publications

References 28 publications

Automatic Table completion using Knowledge Base

Automatic Table completion using Knowledge Base

Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts

Carousel Personalization in Music Streaming Apps with Contextual Bandits

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