Implicit Entity Linking in Tweets

Perera, Sujan; Mendes, Pablo N.; Alex, Adarsh; Sheth, Amit P.; Thirunarayan, Krishnaprasad

doi:10.1007/978-3-319-34129-3_8

Cited by 17 publications

(29 citation statements)

References 10 publications

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“…Since a given query is inherently short, it might not contain sufficient contextual information. Recently, this problem was targeted in other research communities for social media [24] and clinical documents [25], but little work related to QA was done. Thus, we believe that the solutions employed in other research areas can inspire QA researchers.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

Singh¹,

Lytra²,

Radhakrishna³

et al. 2018

Preprint

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Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting and most approaches currently use a pipeline of processing steps connecting a sequence of components. In this article, we analyse and micro evaluate the behaviour of 29 available QA components for the DBpedia knowledge graph that were released by the research community since 2010. As a result, we provide a perspective on collective failure cases, suggest characteristics of QA components that prevent them from performing better and provide future challenges and research directions in the field.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

“…http://docs.aylien.com/docs/introduction (AylienNER)24 The component is similar to the Relation Linker of https://github.com/ dice-group/NLIWOD…”

mentioning

confidence: 99%

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

Singh¹,

Lytra²,

Radhakrishna³

et al. 2018

Preprint

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“…As one of the main prior works, Perera et al [10] have made extensive use of the knowledge graph for building tweet representations. In doing so, they have leveraged explicit mentions of entities within the input tweet.…”

Section: Ad-hoc Retrieval Framework For Linking Implicit Entitiesmentioning

confidence: 99%

Implicit Entity Recognition and Linking in Tweets

Hosseini

2024

Preprint

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Linking textual content to entities from the knowledge graph has received increasing attention where surface form representations of entities are linked to appropriate entities. This allows textual content, e.g., social user-generated content, to be interpreted at a higher semantic level. However, recent research has shown that at least 15% of social user-generated content do not have explicit surface form representation of entities that they discuss. In other words, the subject of the content is only implied. For such cases, existing named entity recognition and linking methods, known as explicit entity linking, cannot perform linking because entity surface form is missing. The objective of this dissertation, while introducing and publicly sharing a comprehensive gold standard dataset for the tasks of implicit named entity recognition and linking, is to propose approaches to these tasks. We formulate the problem of recognizing implicit entity mentions in tweets, where we propose to leverage categorical and linguistically inspired features based on Systemic Functional Linguistics. Our implicit named entity recognizer achieves promising results on different evaluation metrics. Additionally, we propose two approaches for linking implicit mentions in tweets. Within the first, we formulate the problem of implicit entity linking as an ad-hoc document retrieval process where the input query is the tweet, which needs to be implicitly linked and the document space is the set of textual descriptions of entities in the knowledge graph. We systematically compare our work with existing work showing our method is able to provide improvements on a range of retrieval measures. Within the second approach, we model implicit entity linking as a learn to rank problem where knowledge graph entities are ranked based on their relevance to the input tweet. In doing so, we introduce and systematically classify appropriate features for identifying implicit entities. In our experiments, we show that our proposed features are able to improve the state of the art. For SFL-based recognition of implicit entity mentions as well as for the ad-hoc retrieval based and learn to rank based approaches to linking of such mentions, we provide qualitative assessment of the root causes for mislabeled instances in our experiments.

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“…We have developed knowledge-driven solutions that decode the implicit entity mentions in clinical narratives [25] and tweets [26]. We exploit the publicly available knowledge bases (only the portions that matches with the domain of interest) in order to access the required domain knowledge to decode implicitly mentioned entities.…”

Section: Matt Damonmentioning

confidence: 99%

“…2. The text to be recognized is complex (i.e., beyond simple entity -person/location/organization), requiring novel techniques for dealing with complex/compound entities [27], implicit entities [25,26], and subjectivity (emotions, intention) [13,38]. 3.…”

Section: Introductionmentioning

confidence: 99%

Knowledge will propel machine understanding of content

Sheth

Perera

Wijeratne

et al. 2017

Proceedings of the International Conference on Web Intelligence

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Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

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Implicit Entity Linking in Tweets

Cited by 17 publications

References 10 publications

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

Implicit Entity Recognition and Linking in Tweets

Knowledge will propel machine understanding of content

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