An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations

Laskar, Md Tahmid Rahman; Chen, Cheng; Johnston, Jeanne D.; Fu, Xue-Yong; TN, Shashi Bhushan; Corston-Oliver, Simon

doi:10.1145/3477495.3536322

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…In the future, we would like to extend ChartSumm to a multilingual dataset to address the scarcity of well-formatted datasets in other low-resource languages. We will also study how to incorporate query relevance [33][34][35], question-answering [36][37][38][39], and entity recognition [40][41][42] capabilities in this task.…”

Section: Discussionmentioning

confidence: 99%

ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries

Rahman

Hasan

Farhad

et al. 2023

Proceedings of the Canadian Conference on Artificial Intelligence

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Automatic chart to text summarization is an effective tool for the visually impaired people along with providing precise insights of tabular data in natural language to the user. A large and well-structured dataset is always a key part for data driven models. In this paper, we propose ChartSumm: a large-scale benchmark dataset consisting of a total of 84,363 charts along with their metadata and descriptions covering a wide range of topics and chart types to generate short and long summaries. Extensive experiments with strong baseline models show that even though these models generate fluent and informative summaries by achieving decent scores in various automatic evaluation metrics, they often face issues like suffering from hallucination, missing out important data points, in addition to incorrect explanation of complex trends in the charts. We also investigated the potential of expanding ChartSumm to other languages using automated translation tools. These make our dataset a challenging benchmark for future research.

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Section: Discussionmentioning

confidence: 99%

ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries

Rahman

Hasan

Farhad

et al. 2023

Proceedings of the Canadian Conference on Artificial Intelligence

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“…Afterward, each candidate is analyzed in more detail with a cross‐encoder that combines the word and entity text (Wu et al 2020). Laskar, Chen, Martsinovich, et al (2022), Laskar, Chen, Johnston, et al (2022), Bhargav et al (2022) have also employed this method in other studies.…”

Section: Methodsmentioning

confidence: 99%

Multilingual bi‐encoder models for biomedical entity linking

Güven

Lamurias

2023

Expert Systems

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Natural language processing (NLP) is a field of study that focuses on data analysis on texts with certain methods. NLP includes tasks such as sentiment analysis, spam detection, entity linking, and question answering, to name a few. Entity linking is an NLP task that is used to map mentions specified in the text to the entities of a Knowledge Base. In this study, we analysed the efficacy of bi-encoder entity linking models for multilingual biomedical texts. Using surface-based, approximate nearest neighbour search and embedding approaches during the candidate generation phase, accuracy, and recall values were measured on language representation models such as BERT, SapBERT, BioBERT, and RoBERTa according to language and domain. The proposed entity linking framework was analysed on the BC5CDR and Cantemist datasets for English and Spanish, respectively. The framework achieved 76.75% accuracy for the BC5CDR and 60.19% for the Cantemist. In addition, the proposed framework was compared with previous studies. The results highlight the challenges that come with domain-specific multilingual datasets.

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“…Recent contributions have leveraged knowledge from English Wikipedia for diverse tasks, e.g., providing explainable search results (Yu, Rahimi, and Allan 2022), evidence retrieval for fact verification (Chen et al 2022), text stance detection (Zhu et al 2022), sentence retrieval for open-ended dialogues (Harel et al 2022), question answering (Wang, Jatowt, and Yoshikawa 2022;Lerner et al 2022), named entities and relationships retrieval from articles (Laskar et al 2022;Plum et al 2022), etc. However, one of the most important aspects of Wikipedia is its multilingual character.…”

Section: Language-agnostic Approachesmentioning

confidence: 99%

Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages

Das,

Johnson,

Saez-Trumper

et al. 2024

ICWSM

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Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use.

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An Auto Encoder-based Dimensionality Reduction Technique for Efficient Entity Linking in Business Phone Conversations

Cited by 8 publications

References 11 publications

ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries

ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries

Multilingual bi‐encoder models for biomedical entity linking

Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages

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