On a Novel Representation of Multiple Textual Documents in a Single Graph

Giarelis, Nikolaos; Kanakaris, Nikos; Karacapilidis, Nikos

doi:10.1007/978-981-15-5925-9_9

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…The suggested method recognizes a term's significance across the board in a document collection and encourages the inclusion of relationship edges across documents. Experimental results demonstrate that the suggested model outperforms the baseline models with an accuracy score of 97.5% (Giarelis et al 2020 ).…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 98%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 98%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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“…Kallipolitis et al [27] consider a graph that interconnects entities such as patients, encounters, observations, and immunizations with the goal of predicting the risk of a patient's fatality. Giarelis et al propose employing a "graph-of-docs" model to represent documents and their words to enhance text categorization [28] and feature selection [29]. Jalil et al [30] employ word graphs to improve text summarization.…”

Section: Related Workmentioning

confidence: 99%

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

Mongiovì,

Gangemi

2024

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Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents.

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“…To address the drawbacks of the graph-of-words representation, the authors in [7] have proposed the graph-of-docs representation, where multiple textual documents are depicted in a single graph. In this way: (i) the investigation of the importance of a term into a whole corpus of documents is easily calculated, and (ii) the co-existence of heterogeneous nodes in the same graph renders the representation easily expandable and adaptable to more complicated data.…”

Section: Graph-based Text Representationsmentioning

confidence: 99%

“…Aiming to overcome the above issues, this paper proposes the development and deployment of a novel scientific knowledge graph that enables the co-existence and joint utilization of structured as well as unstructured data, such as author, document and word nodes. Expanding on past work, the documents of a scientific graph are represented as a graph of documents (graph-of-docs approach) [6][7][8], which facilitates the discovery of future research collaborations by using prominent link prediction algorithms. In addition, recent advances in graph mining enable our approach to calculate the similarity between two graph-of-docs representations, using graph similarity techniques (e.g., graph kernels and graph neural networks).…”

Section: Introductionmentioning

confidence: 99%

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Kanakaris

Giarelis

Siachos

et al. 2021

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We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

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On a Novel Representation of Multiple Textual Documents in a Single Graph

Cited by 11 publications

References 21 publications

Impact of word embedding models on text analytics in deep learning environment: a review

Impact of word embedding models on text analytics in deep learning environment: a review

GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents

Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

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