A survey on neural relation extraction

Liu, Kang

doi:10.1007/s11431-020-1673-6

Cited by 24 publications

(10 citation statements)

References 38 publications

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“…We identified several surveys that address general relation extraction [50][51][52][53][54][55]. In contrast, there are fewer surveys dedicated to the topics of event extraction [56] and temporal relation extraction [57,58].…”

Section: Hand Crafted Featuresmentioning

confidence: 99%

Event-Centric Temporal Knowledge Graph Construction: A Survey

Knez,

Žitnik

2023

Mathematics

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Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.

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Section: Hand Crafted Featuresmentioning

confidence: 99%

Event-Centric Temporal Knowledge Graph Construction: A Survey

Knez,

Žitnik

2023

Mathematics

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“…Supervised entity relation extraction is essentially a classification task that can be performed using machine-learning models trained using annotated data. Recently, neural network-based models have been widely employed (Liu, 2020). The most significant challenge in supervised entity relation extraction is that annotated training data are scarce in most domains (Vo and Bagheri, 2019).…”

Section: Related Researchmentioning

confidence: 99%

Extracting entity relations for “problem-solving” knowledge graph of scientific domains using word analogy

Chen

Peng

et al. 2022

AJIM

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PurposeProblem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association.Design/methodology/approachThis paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations.FindingsThis paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends.Originality/valueThis paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.

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“…Moreover, this table is also supplemented with core techniques used to solve these problems; it is clearly indicated that deep learning techniques are the most widely researched and applied to solve these problems. For more extensive reviews of the techniques, as well as more discussions on their weaknesses or future prospects, we refer to recent survey NLP papers such as [76,77,78,79,80]. Additionally, their performance can be significantly boosted after applying transfer learning with pretrained language models, such as BERT [39], ELMO [41], RoBERTa [81], ELECTRA [82], XLNet (83), T5 [84] or Microsoft's DeBERTa [85].…”

Section: Introducing Nlp To Model-to-model (M2m) Transformationsmentioning

confidence: 99%

Exploring Natural Language Processing in Model-To-Model Transformations

Danėnas

Skersys

2022

IEEE Access

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In this paper, we explore the possibility to apply natural language processing in visual modelto-model (M2M) transformations. Therefore, we present our research results on information extraction from text labels in process models modeled using Business Process Modeling Notation (BPMN) and use case models depicted in Unified Modeling Language (UML) using the most recent developments in natural language processing (NLP). In this paper, we focus on three relevant tasks, namely, the extraction of verb/noun phrases that would be used to form relations, parsing of conjunctive/disjunctive statements, and the detection of abbreviations and acronyms. Relation extraction was attempted to solve by implementing techniques that combine state-of-the-art NLP language models with formal regular expressions grammarbased structure detection. In this paper, we perform thorough testing of the most recent state-of-the-art NLP tools (CoreNLP, Stanford Stanza, Flair, Spacy, AllenNLP, BERT, ELECTRA), as well as custom BERT-BiLSTM-CRF and ELMo-BiLSTM-CRF implementations, trained with certain data augmentations to improve performance on the most ambiguous cases; these tools are used as a foundation for building tools to extract noun and verb phrases from short text labels generally used in UML and BPMN models. Furthermore, we describe our attempts to improve these extractors by solving the abbreviation/acronym detection problem using machine learning-based detection, as well as process conjunctive and disjunctive statements, due to their relevance to performing advanced text normalization. The obtained results show that the best phrase extraction and conjunctive phrase processing performance was obtained using Stanza based implementation, yet, our trained BERT-BiLSTM-CRF outperformed it for the verb phrase detection task. Our acronym detection approach resulted in the precision of 0.78 and F1-Score of 0.73 which may also be considered quite positive. While this work was inspired by our ongoing research on partial model-to-model transformations, we believe it to be applicable in other areas requiring similar text processing capabilities as well. INDEX TERMSinformation extraction; relation extraction; acronym detection; process models; use-case models; natural language processing; model-to-model transformation This work is inspired by our previous research on NLPenhanced information extraction in model-to-model trans- formations [6, 7]. However, the need for similar solutions was also identified in other areas involving visual modeling, such as business process modeling [8,9,10]. In this paper, we address the issue of relation extraction from graphical

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A survey on neural relation extraction

Cited by 24 publications

References 38 publications

Event-Centric Temporal Knowledge Graph Construction: A Survey

Event-Centric Temporal Knowledge Graph Construction: A Survey

Extracting entity relations for “problem-solving” knowledge graph of scientific domains using word analogy

Exploring Natural Language Processing in Model-To-Model Transformations

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