Implicit entity linking in tweets: An ad-hoc retrieval approach

Hosseini, Hawre; Nguyen, Tam T.; Wu, Jimmy Ming‐Tai; Bagheri, Ebrahim

doi:10.3233/ao-190215

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Le and Titov [8] proposed a neural entity linking model and introduce entity relations, which optimized entity linking in an end-to-end manner with relations as latent variables. Hosseini et al [9] proposed a neural embedding-based feature function that enhances implicit entity linking through prior term dependencies and entitybased feature function interpolation. Shengchen et al [10] proposed a domain-integrated entity linking method based on relation index and representation learning, to address the problem that existing entity linking methods cannot combine text information and knowledge base information well.…”

Section: Introductionmentioning

confidence: 99%

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Wang

Zhou

et al. 2022

Journal of Sensors

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To solve the problem of unclear entity boundaries and low recognition accuracy in Chinese text, we construct the crop dataset and propose a Bert-binary-based entity link method. Candidate entity sets are generated through entity matching in multiple data sources. The Bert-binary model is called to calculate the correct probability of the candidate entity, and the entity with the highest score is screened for linking. In comparative experiments with three models on the crop dataset, the F 1 value is increased by 2.5% on the best method or by 8.8% on average. The experimental results show the effectiveness of Bert-binary method in this paper.

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Section: Introductionmentioning

confidence: 99%

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Wang

Zhou

et al. 2022

Journal of Sensors

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“…This is while our later study showed that implicit entity instances on Twitter span many other domains [11]. Moreover, this dataset only shares tweets with implicit mention making it hard to build training sets for machine learning applications.…”

Section: Taxonomies and Datasetsmentioning

confidence: 93%

“…• [21] Hosseini, H., Nguyen, T. T., Wu, J., Bagheri, E. (2019). Implicit entity linking in tweets: An ad-hoc retrieval approach.…”

Section: Publicationsmentioning

confidence: 99%

“…The task of Named Entity Recognition and Linking, as a major component of information extraction pipelines, has been performed with considerably good performances for Twitter content [6,7,8], despite several challenges such contents pose to natural language understanding tools due to the tweets' language and length properties [9]. However, according to recent studies, a noticeable percentage of tweets contain implicit references to the entity being discussed, referred to as implicit named entity mentions [10,11,12]. Such named entity mentions lack a surface form but comprise the core theme of the tweet.…”

Section: Introduction 11 Motivationmentioning

confidence: 99%

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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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“…In "Implicit Entity Linking in Tweets: an Ad-hoc Retrieval Approach" Hosseini et al (2019) explore the Natural Language Processing task of identifying entities in text that are only indirectly mentioned, e.g., in the utterance "Angelina Jolie and her husband", denoting Brad Pitt. In the proposed approach, explicitly mentioned entities are automatically linked to a knowledge base, and they provide the context for the disambiguation of the implicit entity.…”

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confidence: 99%

Meaning in Context: Ontologically and linguistically motivated representations of objects and events

Basile

Caselli

Radicioni

2019

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Context is pervasive in all activities involving human beings as well as computer systems. It affects numerous aspects of our lives: how we understand the world, our communication, as well as the planning, carrying, and outcomes of activities. When focusing on understanding natural language, context plays a pivotal role that affects various facets ranging from the interpretation of speech signal to the identification of word meaning, composition of phrases and sentences as well as the intention of the discourse.Context impacts the conceptualisation of human experience. Objects and events assume different cognitive salience according to their context of occurrence, thus determining access to partial relevant information rather than to all information. Understanding a piece of natural language requires a continuous interaction between abstract conceptualisations and their actual use or occurrence in a specific communicative situation. A possible example to clarify this distinction could be that of an orange being passed between two children, and that of the same orange peeled on a table: in the former context the roundness of the object prevails over other features, traits, or aspects that characterise the concept of "the fruit orange". In the latter context, the edible features are those mostly relevant and conveyed. Similar mechanisms are at play with events. A well known example is that of "commercial events". Different surface forms (i.e., verbs) can be used to express related, if not the same, knowledge about the same scenario. For instance, the verbs buy and sell offer different interpretations of the same event, where none of them puts the focus on the object of the transaction, but rather on its participant. An extreme situation, in this case, is when different interpretations are imposed on the same event. For instance, a fatal shooting by the police can be interpreted as necessary self-defence (by the police officers) or as a murder (by the friends and family of the victim). Similar selectional mechanisms underlie figurative uses of word meanings, such as metonymy and metaphors among others, that intrinsically characterize the interface between knowledge and language -see the work by Gangemi et al. (2018) for a recent study on the ontological treatment of conceptual metaphors.

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Implicit entity linking in tweets: An ad-hoc retrieval approach

Cited by 8 publications

References 26 publications

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

Implicit Entity Recognition and Linking in Tweets

Meaning in Context: Ontologically and linguistically motivated representations of objects and events

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