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1 – 10 of over 5000
Article
Publication date: 3 August 2021

Chuanming Yu, Haodong Xue, Manyi Wang and Lu An

Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From…

Abstract

Purpose

Owing to the uneven distribution of annotated corpus among different languages, it is necessary to bridge the gap between low resource languages and high resource languages. From the perspective of entity relation extraction, this paper aims to extend the knowledge acquisition task from a single language context to a cross-lingual context, and to improve the relation extraction performance for low resource languages.

Design/methodology/approach

This paper proposes a cross-lingual adversarial relation extraction (CLARE) framework, which decomposes cross-lingual relation extraction into parallel corpus acquisition and adversarial adaptation relation extraction. Based on the proposed framework, this paper conducts extensive experiments in two tasks, i.e. the English-to-Chinese and the English-to-Arabic cross-lingual entity relation extraction.

Findings

The Macro-F1 values of the optimal models in the two tasks are 0.880 1 and 0.789 9, respectively, indicating that the proposed CLARE framework for CLARE can significantly improve the effect of low resource language entity relation extraction. The experimental results suggest that the proposed framework can effectively transfer the corpus as well as the annotated tags from English to Chinese and Arabic. This study reveals that the proposed approach is less human labour intensive and more effective in the cross-lingual entity relation extraction than the manual method. It shows that this approach has high generalizability among different languages.

Originality/value

The research results are of great significance for improving the performance of the cross-lingual knowledge acquisition. The cross-lingual transfer may greatly reduce the time and cost of the manual construction of the multi-lingual corpus. It sheds light on the knowledge acquisition and organization from the unstructured text in the era of big data.

Details

The Electronic Library , vol. 39 no. 3
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 22 October 2021

Na Pang, Li Qian, Weimin Lyu and Jin-Dong Yang

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been…

Abstract

Purpose

In computational chemistry, the chemical bond energy (pKa) is essential, but most pKa-related data are submerged in scientific papers, with only a few data that have been extracted by domain experts manually. The loss of scientific data does not contribute to in-depth and innovative scientific data analysis. To address this problem, this study aims to utilize natural language processing methods to extract pKa-related scientific data in chemical papers.

Design/methodology/approach

Based on the previous Bert-CRF model combined with dictionaries and rules to resolve the problem of a large number of unknown words of professional vocabulary, in this paper, the authors proposed an end-to-end Bert-CRF model with inputting constructed domain wordpiece tokens using text mining methods. The authors use standard high-frequency string extraction techniques to construct domain wordpiece tokens for specific domains. And in the subsequent deep learning work, domain features are added to the input.

Findings

The experiments show that the end-to-end Bert-CRF model could have a relatively good result and can be easily transferred to other domains because it reduces the requirements for experts by using automatic high-frequency wordpiece tokens extraction techniques to construct the domain wordpiece tokenization rules and then input domain features to the Bert model.

Originality/value

By decomposing lots of unknown words with domain feature-based wordpiece tokens, the authors manage to resolve the problem of a large amount of professional vocabulary and achieve a relatively ideal extraction result compared to the baseline model. The end-to-end model explores low-cost migration for entity and relation extraction in professional fields, reducing the requirements for experts.

Details

Data Technologies and Applications, vol. 56 no. 2
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 14 May 2019

Ahsan Mahmood, Hikmat Ullah Khan, Zahoor Ur Rehman, Khalid Iqbal and Ch. Muhmmad Shahzad Faisal

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named…

Abstract

Purpose

The purpose of this research study is to extract and identify named entities from Hadith literature. Named entity recognition (NER) refers to the identification of the named entities in a computer readable text having an annotation of categorization tags for information extraction. NER is an active research area in information management and information retrieval systems. NER serves as a baseline for machines to understand the context of a given content and helps in knowledge extraction. Although NER is considered as a solved task in major languages such as English, in languages such as Urdu, NER is still a challenging task. Moreover, NER depends on the language and domain of study; thus, it is gaining the attention of researchers in different domains.

Design/methodology/approach

This paper proposes a knowledge extraction framework using finite-state transducers (FSTs) – KEFST – to extract the named entities. KEFST consists of five steps: content extraction, tokenization, part of speech tagging, multi-word detection and NER. An extensive empirical analysis using the data corpus of Urdu translation of Sahih Al-Bukhari, a widely known hadith book, reveals that the proposed method effectively recognizes the entities to obtain better results.

Findings

The significant performance in terms of f-measure, precision and recall validates that the proposed model outperforms the existing methods for NER in the relevant literature.

Originality/value

This research is novel in this regard that no previous work is proposed in the Urdu language to extract named entities using FSTs and no previous work is proposed for Urdu hadith data NER.

Details

The Electronic Library , vol. 37 no. 2
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 1 May 2020

Qihang Wu, Daifeng Li, Lu Huang and Biyun Ye

Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities

Abstract

Purpose

Entity relation extraction is an important research direction to obtain structured information. However, most of the current methods are to determine the relations between entities in a given sentence based on a stepwise method, seldom considering entities and relations into a unified framework. The joint learning method is an optimal solution that combines relations and entities. This paper aims to optimize hierarchical reinforcement learning framework and provide an efficient model to extract entity relation.

Design/methodology/approach

This paper is based on the hierarchical reinforcement learning framework of joint learning and combines the model with BERT, the best language representation model, to optimize the word embedding and encoding process. Besides, this paper adjusts some punctuation marks to make the data set more standardized, and introduces positional information to improve the performance of the model.

Findings

Experiments show that the model proposed in this paper outperforms the baseline model with a 13% improvement, and achieve 0.742 in F1 score in NYT10 data set. This model can effectively extract entities and relations in large-scale unstructured text and can be applied to the fields of multi-domain information retrieval, intelligent understanding and intelligent interaction.

Originality/value

The research provides an efficient solution for researchers in a different domain to make use of artificial intelligence (AI) technologies to process their unstructured text more accurately.

Details

Information Discovery and Delivery, vol. 48 no. 3
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 28 December 2023

Na Xu, Yanxiang Liang, Chaoran Guo, Bo Meng, Xueqing Zhou, Yuting Hu and Bo Zhang

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a…

Abstract

Purpose

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Design/methodology/approach

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Findings

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

Originality/value

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 20 September 2022

Jinzhu Zhang, Yue Liu, Linqi Jiang and Jialu Shi

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic…

Abstract

Purpose

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Design/methodology/approach

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

Findings

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

Originality/value

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

Details

Aslib Journal of Information Management, vol. 75 no. 3
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 24 June 2020

Yilu Zhou and Yuan Xue

Strategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and…

234

Abstract

Purpose

Strategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and has limited scope. This paper proposes a text-mining framework, ACRank, that automatically extracts alliances from news articles. ACRank aims to provide human analysts with a higher coverage of strategic alliances compared to existing databases, yet maintain a reasonable extraction precision. It has the potential to discover alliances involving less well-known companies, a situation often neglected by commercial databases.

Design/methodology/approach

The proposed framework is a systematic process of alliance extraction and validation using natural language processing techniques and alliance domain knowledge. The process integrates news article search, entity extraction, and syntactic and semantic linguistic parsing techniques. In particular, Alliance Discovery Template (ADT) identifies a number of linguistic templates expanded from expert domain knowledge and extract potential alliances at sentence-level. Alliance Confidence Ranking (ACRank)further validates each unique alliance based on multiple features at document-level. The framework is designed to deal with extremely skewed, noisy data from news articles.

Findings

In evaluating the performance of ACRank on a gold standard data set of IBM alliances (2006–2008) showed that: Sentence-level ADT-based extraction achieved 78.1% recall and 44.7% precision and eliminated over 99% of the noise in news articles. ACRank further improved precision to 97% with the top20% of extracted alliance instances. Further comparison with Thomson Reuters SDC database showed that SDC covered less than 20% of total alliances, while ACRank covered 67%. When applying ACRank to Dow 30 company news articles, ACRank is estimated to achieve a recall between 0.48 and 0.95, and only 15% of the alliances appeared in SDC.

Originality/value

The research framework proposed in this paper indicates a promising direction of building a comprehensive alliance database using automatic approaches. It adds value to academic studies and business analyses that require in-depth knowledge of strategic alliances. It also encourages other innovative studies that use text mining and data analytics to study business relations.

Details

Information Technology & People, vol. 33 no. 5
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 14 November 2023

Shaodan Sun, Jun Deng and Xugong Qin

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained…

Abstract

Purpose

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

Design/methodology/approach

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

Findings

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Originality/value

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 27 July 2022

Svetlozar Nestorov, Dinko Bačić, Nenad Jukić and Mary Malliaris

The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles.

Abstract

Purpose

The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles.

Design/methodology/approach

The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module.

Findings

This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization.

Originality/value

Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.

Article
Publication date: 8 June 2022

Guo Chen, Jiabin Peng, Tianxiang Xu and Lu Xiao

Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by…

Abstract

Purpose

Problem-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/approach

This 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.

Findings

This 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/value

This 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.

Details

Aslib Journal of Information Management, vol. 75 no. 3
Type: Research Article
ISSN: 2050-3806

Keywords

1 – 10 of over 5000