Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding

Ji, Lei; Wang, Yujing; Shi, Botian; Zhang, Dawei; Wang, Zhongyuan; Yan, Jun

doi:10.1162/dint_a_00013

Cited by 36 publications

(22 citation statements)

References 17 publications

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“…Nouns in sentences are extracted with SpaCy and generalized using the Microsoft Concept Graph [9] by "is a" concept. A feature selection approach is used to reduce the number of features.…”

Section: Tax2vec Featuresmentioning

confidence: 99%

Identification of COVID-19 Related Fake News via Neural Stacking

Koloski

Stepišnik-Perdih

Pollak

et al. 2021

Combating Online Hostile Posts in Regional Languages During Emergency Situation

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Identification of Fake News plays a prominent role in the ongoing pandemic, impacting multiple aspects of day-to-day life. In this work we present a solution to the shared task titled COVID19 Fake News Detection in English, scoring the 50th place amongst 168 submissions. The solution was within 1.5% of the best performing solution. The proposed solution employs a heterogeneous representation ensemble, adapted for the classification task via an additional neural classification head comprised of multiple hidden layers. The paper consists of detailed ablation studies further displaying the proposed method's behavior and possible implications. The solution is freely available. https://gitlab.com/boshko.koloski/covid19-fake-news

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“…Nouns in sentences are extracted with SpaCy and generalized using the Microsoft Concept Graph [9] by "is a" concept. A feature selection approach is used to reduce the number of features.…”

Section: Tax2vec Featuresmentioning

confidence: 99%

Identification of COVID-19 Related Fake News via Neural Stacking

Koloski

Stepišnik-Perdih

Pollak

et al. 2021

Combating Online Hostile Posts in Regional Languages During Emergency Situation

View full text Add to dashboard Cite

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“…The concept graph is constructed to empower models with the knowledge about the concepts in the real world. Existing works mainly focus on extracting the relations between entities in a text and concepts [15,19,43]. Instead, our main task is to conceptualize a document by tagging the sentences in the document with the most semantically relevant concepts, aided by the concept graph directly sampled from Attention Graph [20].…”

Section: Related Workmentioning

confidence: 99%

TAG: Toward Accurate Social Media Content Tagging with a Concept Graph

Yang¹,

Guo²,

Liu³

et al. 2021

Preprint

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Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media. This is partly attributed to the fact that most knowledge bases contain general terms of the world, such as trees and cars, which do not have the defining power or are not interesting enough to social media app users. Another reason is that the intricacy of natural language allows the use of tense, negation and grammar to change the logic or emphasis of language, thus conveying completely different meanings. In this paper, we present TAG, a high-quality concept matching dataset consisting of 10,000 labeled pairs of fine-grained concepts and web-styled natural language sentences, mined from the open-domain social media. The concepts we consider represent the trending interests of online users. Associated with TAG is a concept graph of these fine-grained concepts and entities to provide the structural context information. We evaluate a wide range of popular neural text matching models as well as pre-trained language models on TAG, and point out their insufficiency to tag social media content with the most appropriate concept. We further propose a novel graph-graph matching method that demonstrates superior abstraction and generalization performance by better utilizing both the structural context in the concept graph and logic interactions between semantic units in the sentence via syntactic dependency parsing.

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“…Secondly, we obtain relevant concept for the extracted entities. ConceptNet [20] and Microsoft Concept Graph [21][22][23][24][25][26][27] are the two widely used toolkits to obtain the concept of an object. We choose to use the Microsoft Concept Graph, which has 5.3 million concepts learned from billions of website pages and search logs for the conceptualization.…”

Section: Semantic Information Retrieval Modulementioning

confidence: 99%

Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

2020

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Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.

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Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding

Cited by 36 publications

References 17 publications

Identification of COVID-19 Related Fake News via Neural Stacking

Identification of COVID-19 Related Fake News via Neural Stacking

TAG: Toward Accurate Social Media Content Tagging with a Concept Graph

Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

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