Information Extraction Research Research Articles

Parsing of Research Documents into XML Using Formal Grammars

Automatic information extraction of content and style format in paged documents is challenging. It requires the conversion of the original document into a granular level of details for which every document section and content is identifiable. This functionality or tool does not exist for any academic research document yet. In this paper, we present an automated process of parsing research paper documents into XML files using a formal method approach of context-free grammars (CFGs) and regular expressions (REGEXs) definable of a standard template. We created a tool for the algorithms to parse these documents into tree-like structures organized as XML files named research_XML (RX) parser. The RX tool performed the extraction of syntactic structure and semantic information of the document’s contents into XML files. These XML output files are lightweight, analyzable, query-able, and web interoperable. The RX tool has a success rate of 91% when evaluated on fifty varying research documents of 160 average pages and 8,004 total pages. The tool and test data are accessible on GitHub repo. The novelty of our process is specific to applying formal techniques for information extraction in structured multipaged documents and academic research documents thus advancing the research in automatic information extraction.

Construction of cardiovascular information extraction corpus based on electronic medical records.

Cardiovascular disease has a significant impact on both society and patients, making it necessary to conduct knowledge-based research such as research that utilizes knowledge graphs and automated question answering. However, the existing research on corpus construction for cardiovascular disease is relatively limited, which has hindered further knowledge-based research on this disease. Electronic medical records contain patient data that span the entire diagnosis and treatment process and include a large amount of reliable medical information. Therefore, we collected electronic medical record data related to cardiovascular disease, combined the data with relevant work experience and developed a standard for labeling cardiovascular electronic medical record entities and entity relations. By building a sentence-level labeling result dictionary through the use of a rule-based semi-automatic method, a cardiovascular electronic medical record entity and entity relationship labeling corpus (CVDEMRC) was constructed. The CVDEMRC contains 7691 entities and 11,185 entity relation triples, and the results of consistency examination were 93.51% and 84.02% for entities and entity-relationship annotations, respectively, demonstrating good consistency results. The CVDEMRC constructed in this study is expected to provide a database for information extraction research related to cardiovascular diseases.

PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions

PurposeBy far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language.Design/methodology/approachThe methodology is divided into corpus development and extraction model development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration, model training and evaluation, as well as model selection.FindingsThe Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus. Furthermore, the baseline model predicted most labels and achieved 82.026% F-score.Originality/valueTo the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented in this paper to develop a new corpus and baseline model for other languages.

Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching

As one of the hotspots in music information extraction research, music recognition has received extensive attention from scholars in recent years. Most of the current research methods are based on traditional signal processing methods, and there is still a lot of room for improvement in recognition accuracy and recognition efficiency. There are few research studies on music recognition based on deep neural networks. This paper expounds on the basic principles of deep learning and the basic structure and training methods of neural networks. For two kinds of commonly used deep networks, convolutional neural network and recurrent neural network, their typical structures, training methods, advantages, and disadvantages are analyzed. At the same time, a variety of platforms and tools for training deep neural networks are introduced, and their advantages and disadvantages are compared. TensorFlow and Keras frameworks are selected from them, and the practice related to neural network research is carried out. Training lays the foundation. Results show that through the development and experimental demonstration of the prototype system, as well as the comparison with other researchers in the field of humming recognition, it is proved that the deep-learning method can be applied to the humming recognition problem, which can effectively improve the accuracy of humming recognition and improve the recognition time. A convolutional recurrent neural network is designed and implemented, combining the local feature extraction of the convolutional layer and the ability of the recurrent layer to summarize the sequence features, to learn the features of the humming signal, so as to obtain audio features with a higher degree of abstraction and complexity and improve the performance of the humming signal. The ability of neural networks to learn the features of audio signals lays the foundation for an efficient and accurate humming recognition process.

Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug Instructions

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction. In addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 3 0.95) and their relations (F1 3 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.

Supervised Learning Approaches for Nested People Entity Extraction in Indonesian Translated Quran

Since the Quran is the primary holy book for Muslims, information extraction research on Quranic texts, especially in a form of People Entity Extraction, is an important task for further Quran and Tafseer understanding. The challenges in extracting people entities from the Quranic text is that many verses have a complex structure, such as nested entities, making it crucial to build a system that can extract the entity automatically, accurately, and quickly. People Entity Extraction on Quran itself is a task that aims to extract people entities in a sentence or verse, such as the name of a person, the name of a group, etc. on the Quranic texts. Example of input taken from snippet Surah Al-Baqarah verse 46 which reads “Those who believe that they will meet their Lord and that they will return to him” from that input the people entity extraction system is expected can identify people entities i.e. “Those who believe that they will meet their Lord”. Currently, People Entity Extraction research for the Quran has not been widely carried out, only a few algorithms with scattered results have been conducted. In this research, we will use several supervised models which are Conditional Random Field (CRF), BiLSTM-CRF, and a pre-trained deep learning model based on IndoBERT transformers. We apply and perform a comparative analysis for the performance of those several models. We found out that deep learning based model, namely BiLSTM-CRF perform best at extracting people entities, whilst probabilistic based model, namely CRF, had difficulty in extracting people entities, specifically nested people entities.

Machine learning algorithm for information extraction from gynaecological domain in Tamil

Information Extraction is a significant task in Natural Language Processing. It is the process of extracting useful information from unstructured text. Information extraction helps in most of the recent NLP applications like scientific research, financial investigation, business intelligence, media monitoring, healthcare records management, agriculture, and pharmacy research. There are several information extraction research approaches using many techniques from English dataset. As a multi lingual country India, it is actually challenging task to extract information from text in Indian language. Such research work has been done in the following domain data travel, food, agriculture, weather casting, social media, marketing and bio- medical. In this research work the relevant information extracted from Gynaecology related text data in Tamil language. The combination of machine learning based classification model and ontology representation is used extract the useful information. Auto filling IE framework is designed to extract the appropriate information in a structured format. The user query will be pre-processed and converted into entities to check from the classified data using ontological representation by using machine learning based classification model naive bayes classification. From the ontological representation entity based relation extraction will be performed to fill the IE framework. The proposed IE framework given good results in extracting relevant information based on user query. It was analyzed for more than 57 user queries regarding gynaecological issues. The 75% of accuracy obtained for the correctness in user queries.

Machine learning algorithm for information extraction from gynaecological domain in Tamil

Information Extraction is a significant task in Natural Language Processing. It is the process of extracting useful information from unstructured text. Information extraction helps in most of the recent NLP applications like scientific research, financial investigation, business intelligence, media monitoring, healthcare records management, agriculture, and pharmacy research. There are several information extraction research approaches using many techniques from English dataset. As a multi lingual country India, it is actually challenging task to extract information from text in Indian language. Such research work has been done in the following domain data travel, food, agriculture, weather casting, social media, marketing and bio- medical. In this research work the relevant information extracted from Gynaecology related text data in Tamil language. The combination of machine learning based classification model and ontology representation is used extract the useful information. Auto filling IE framework is designed to extract the appropriate information in a structured format. The user query will be pre-processed and converted into entities to check from the classified data using ontological representation by using machine learning based classification model naïve bayes classification. From the ontological representation entity based relation extraction will be performed to fill the IE framework. The proposed IE framework given good results in extracting relevant information based on user query. It was analyzed for more than 57 user queries regarding gynaecological issues. The 75% of accuracy obtained for the correctness in user queries.

Information Extraction from Text Intensive and Visually Rich Banking Documents

Document types, where visual and textual information plays an important role in their analysis and understanding, pose a new and attractive area for information extraction research. Although cheques, invoices, and receipts have been studied in some previous multi-modal studies, banking documents present an unexplored area due to the naturalness of the text they possess in addition to their visual richness. This article presents the first study which uses visual and textual information for deep-learning based information extraction on text-intensive and visually rich scanned documents which are, in this instance, unstructured banking documents, or more precisely, money transfer orders. The impact of using different neural word representations (i.e., FastText, ELMo, and BERT) on IE subtasks (namely, named entity recognition and relation extraction stages), positional features of words on document images and auxiliary learning with some other tasks are investigated. The article proposes a new relation extraction algorithm based on graph factorization to solve the complex relation extraction problem where the relations within documents are n-ary, nested, document-level, and previously indeterminate in quantity. Our experiments revealed that the use of deep learning algorithms yielded around 10 percentage points improvement on the IE sub-tasks. The inclusion of word positional features yielded around 3 percentage points of improvement in some specific information fields. Similarly, our auxiliary learning experiments yielded around 2 percentage points of improvement on some information fields associated with the specific transaction type detected by our auxiliary task. The integration of the information extraction system into a real banking environment reduced cycle times substantially. When compared to the manual workflow, document processing pipeline shortened book-to-book money transfers to 10 minutes (from 29 min.) and electronic fund transfers (EFT) to 17 minutes (from 41 min.) respectively.

Research on Marine Radar Image Processing Method in Sea Ice Information Extraction Process

Firstly, the effectiveness of sea ice information extraction research with navigation radar is proposed. Different methods of image pre-processing are used for different sea ice information elements, and then the functions and characteristics of various processing methods are respectively summarized. The above processing methods, while taking advantage of the convenience of obtaining image data by shipborne navigation radar, can successfully process the radar image through MATLAB and provide a basis for the next extraction of sea ice information and further expansion of research on sea ice monitoring by marine radar.

Deep learning-based extraction of construction procedural constraints from construction regulations

Construction procedural constraints are critical in facilitating effective construction procedure checking in practice and for various inspection systems. Nowadays, the manual extraction of construction procedural constraints is costly and time-consuming. The automatic extraction of construction procedural constraint knowledge (e.g., knowledge entities and interlinks/relationships between them) from regulatory documents is a key challenge. Traditionally, natural language processing is implemented using either rule-based or machine learning approaches. Limited efforts on rule-based extraction of construction regulations often rely on pre-defined vocabularies and involve heavy feature engineering. Based on characteristics of the knowledge expression of construction procedural constraints in Chinese regulations, this paper explores a hybrid deep neural network, combining the bidirectional long short-term memory (Bi-LSTM) and the conditional random field (CRF), for the automatic extraction of the qualitative construction procedural constraints. Based on the proposed deep neural network, the recognition and extraction of named entities and relations between them are realized. Unlike existing information extraction research efforts using rule-based methods, the proposed hybrid deep learning approach can be applied without complex handcrafted features engineering. Besides, the long distance dependency relationships between different entities in regulations are considered. The model implementation results demonstrate the good performance of the end-to-end deep neural network in the extraction of construction procedural constraints. This study can be considered as one of the early explorations of knowledge extraction from construction regulations.

What Can Be Accomplished with the State of the Art in Information Extraction? A Personal View

Though information extraction (IE) research has more than a 25-year history, F1 scores remain low. Thus, one could question continued investment in IE research. In this article, we present three applications where information extraction of entities, relations, and/or events has been used, and note the common features that seem to have led to success. We also identify key research challenges whose solution seems essential for broader successes. Because a few practical deployments already exist and because breakthroughs on particular challenges would greatly broaden the technology’s deployment, further R&D investments are justified.

Support Vector Machine with Ensemble Tree Kernel for Relation Extraction.

Relation extraction is one of the important research topics in the field of information extraction research. To solve the problem of semantic variation in traditional semisupervised relation extraction algorithm, this paper proposes a novel semisupervised relation extraction algorithm based on ensemble learning (LXRE). The new algorithm mainly uses two kinds of support vector machine classifiers based on tree kernel for integration and integrates the strategy of constrained extension seed set. The new algorithm can weaken the inaccuracy of relation extraction, which is caused by the phenomenon of semantic variation. The numerical experimental research based on two benchmark data sets (PropBank and AIMed) shows that the LXRE algorithm proposed in the paper is superior to other two common relation extraction methods in four evaluation indexes (Precision, Recall, F-measure, and Accuracy). It indicates that the new algorithm has good relation extraction ability compared with others.

Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

Characterized by small size, light weight and large transmission ratio, planetary gear transmission is widely used in large scale complex mechanical system with low speed and heavy duty. However, due to the influences of operating condition, manufacturing error, assembly error and multi-tooth meshing, the vibration signal of planetary gear exhibits the characteristics of nonlinear and non-stationary. Especially when early gear fault occurs, the weak fault feature information is submerged in interfering signal. A weak fault feature information extraction method of planetary gear based on Ensemble Empirical Mode Decomposition (EEMD) and Adaptive Stochastic Resonance (ASR) is proposed. The original signal is decomposed to the Intrinsic Mode Functions (IMFs) with small modal aliasing by EEMD. The Signal to Noise Ratio (SNR) of fault feature frequency information of each IMF is calculated, and the IMFs with first four higher SNR are reconstructed and selected as the effective IMFs containing main fault feature information. ASR system is built by combining Particle Swarm Optimization (PSO) and Stochastic Resonance (SR). PSO algorithm is used to optimize the critical parameters of SR, and SNR of ASR output signal is defined as an optimization objective. When the signal reconstructed by effective IMFs is inputted into ASR system, the weak fault feature information can be extracted from the output signal of ASR system. The experimental results show that the proposed method can extract the weak fault feature information of normal gear and fault gears successfully. The amplitudes of fault feature frequency and its sidebands generated by planetary gear fault have a significantly increase, and the effects on sideband amplitudes of faults become even greater than that on the amplitude of fault feature frequency. For different gear faults, the amplitude of fault feature frequency has different changes, meanwhile different sidebands are produced. Planetary gear fault diagnosis can be achieved accurately by comparing the extracted weak fault feature information, so it is an effective method of weak fault feature information extraction of planetary gear.

Ontology-Based Information Extraction with a Cognitive Agent

Machine reading is a relatively new field that features computer programs designed to read flowing text and extract fact assertions expressed by the narrative content. This task involves two core technologies: natural language processing (NLP) and information extraction (IE). In this paper we describe a machine reading system that we have developed within a cognitive architecture. We show how we have integrated into the framework several levels of knowledge for a particular domain, ideas from cognitive semantics and construction grammar, plus tools from prior NLP and IE research. The result is a system that is capable of reading and interpreting complex and fairly idiosyncratic texts in the family history domain. We describe the architecture and performance of the system. After presenting the results from several evaluations that we have carried out, we summarize possible future directions.

Ontology Self-Perfect Based Lexical Chain for IE

The perfection of an ontology knowledge base is essential to the research of ontology-based information extraction (IE). Information extraction in short documents with sparse vocabularies in the ontology will cause the problem of semantic deviation. That will affect the indexes of information extraction in short documents. In this paper, we propose a method of using lexical chain to perfect the ontology knowledge base automatically in order to cover the shortage of manual constructed ontology. We can solve the problem of semantic information deficiency caused by the sparse vocabulary in the ontology effectively through the use of this method. We proved the validity of our method through the series of experiments we conducted.

The Research of Information Extraction in Information System from SQL Server Based on System Table

This article describes the information extraction in information system from SQL Server. Through the system tables all information of SQL Server's internal objects in information system can be obtained, including databases, data tables, stored procedures and triggers and so on. Analysis the method of the object’s information extraction in information system. Describe the relationships of system tables in SQL Server through the graph and table in detail. A visual basic procedure using system table method is designed to achieve the table’s structure information for all user tables in specified SQL Server database. This method is more complete than the method based on ADOX.

PROJECTING NAMED ENTITY TAGS FROM A RESOURCE RICH LANGUAGE TO A RESOURCE POOR LANGUAGE

Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. The English corpus is the translated version of the Malay corpus. Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping. The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure. An evaluation of the selected open source NER tool for English is also presented.

Research of Information Extraction from SQL Server Based on SQL-DMO

This article describes the SQL Server object model SQL-DMO. Through the object model all information of SQL Server's internal objects can be obtained, including databases, data tables, stored procedures and triggers and so on. Analysis the method of the object’s information extraction. Get the child object by analyzing the parent object’s collection property and get internal information by analyzing the object’s properties. A visual basic procedure is designed to achieve the table’s structure information for all user tables in specified SQL Server database. This method is more complete and simple than the method based on system table.

Gurbantunggut Desert Haloxylon Forest NDVI Time Effect - Based on Phenological Changes and the Image of MODIS

Remote sensing image information extraction research is one of the key problems of remote sensing research, it is also one of the hot and difficult points in remote sensing research., In this paper,NDVI images for the study of synthesis of haloxylon ammdendron forest 16 days of MODIS is taken as research object, aimed at improving the precision of information extraction, focuses on the NDVI time effects on phenology of haloxylon ammdendron woodland response, tell woodland diagnosis point of information, using diagnostic points of haloxylon forest information analysis and extraction of results and field visits, mutatis mutandis, the results showed information extraction accuracy has improved.