Entity Extraction from Unstructured Data on the Web

Stemming is a basic morphological analysis tool or a process used to remove variants of a particular word and to identify its root or stem. In this, morphological analysis is carried out to identify various forms of a word to obtain its stem. Stemming is used as a preprocessing operational tool for the development of various natural language text applications, such as part-of-speech tagging, sentiment analysis, text segmentation, text classification, text summarization, information extraction, information retrieval applications, and named entity recognition. Stemming is generally used to achieve better performance and evaluation of information retrieval systems. It is achieved by increasing the standard in the format of the text. There may be various forms of the same root word or base that are used in the system database to increase storage requirements by stemming. Index files are reduced to the words present in the text with their linguistic variants in the documents. It is useful for memory management and system performance and for reducing memory requirements. Our proposed system is designed specifically for Marathi language text because for some other languages in Deonagri scripts like Hindi or Konkani stemmers are available and have good results and performance. But for Marathi, very little work has been done on stemmers. The proposed idea is to develop a stemmer for the Marathi language by using a supervised machine learning approach and our own developed handwritten grammar rule set and word dictionary using which the suffixes of the different variants are successfully removed for generating their respective stem words. Finally, the system is evaluated for its performance. It is measured at 61.34%.KeywordsNLPStemmingLinguistic variantsSuffix removalDeonagri scripts

Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understanding. Most classical approaches use a sequence-based model (typically BiLSTM-CRF framework) without considering document structure. Recent work on graph-based model using graph convolutional networks to encode visual and textual features have achieved promising performance on the task. However, few attempts take geometry information of text segments (text in bounding box) in visual documents into account. Meanwhile, existing methods do not consider that related text segments which need to be merged to form a complete entity in many real-world situations. In this paper, we present GraphNEMR, a graph-based model that uses graph convolutional networks to jointly merge text segments and recognize named entities. By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations. To merge text segments, we introduce a novel mechanism that captures both geometry information as well as semantic information based on pre-trained language model. Experimental results show that the proposed GraphNEMR model outperforms both sequence-based and graph-based SOTA methods significantly.

Named entity recognition (NER) from visual documents, such as invoices, receipts or business cards, is a critical task for visual document understanding. Most classical approaches use a sequence-based model (typically BiLSTM-CRF framework) without considering document structure. Recent work on graph-based model using graph convolutional networks to encode visual and textual features have achieved promising performance on the task. However, few attempts take geometry information of text segments (text in bounding box) in visual documents into account. Meanwhile, existing methods do not consider that related text segments which need to be merged to form a complete entity in many real-world situations. In this paper, we present GraphNEMR, a graph-based model that uses graph convolutional networks to jointly merge text segments and recognize named entities. By incorporating geometry information from visual documents into our model, richer 2D context information is generated to improve document representations. To merge text segments, we introduce a novel mechanism that captures both geometry information as well as semantic information based on pre-trained language model. Experimental results show that the proposed GraphNEMR model outperforms both sequence-based and graph-based SOTA methods significantly.

A complete inpatient electronic medical record contains a lot of information. In recent years, numerous researchers have carried out research on word segmentation of medical texts. Since the medical record text is written by free text, structuring the medical record text is an important part of the medical record intelligent analysis when these medical named entities are recognized. At present, named entity recognition methods are mainly divided into lexicographical and rule-based methods and machine learning-based methods. The machine learn-based approach takes the named entity recognition task as the annotation problem of sequence data, mainly considering the context information. The features commonly used in feature construction are contextual feature and dictionary features. BERT+LSTM+CRF were used to train the named entity recognition model. Open source CRF++ was adopted as the tool we relied on. We trained the LSTM+F model using the results of the original word segmentation and the information in the context as features. We carried out a 5-fold cross validation. The results showed that the overall F-1 score (MICRO-F) of named entity recognition reached 0.92, which confirmed that the model could accurately complete the task of medical named entity recognition.KeywordsWord segmentationNamed entity recognitionNatural language processing

In the field of Natural Language Processing, one of the very important research areas of Information Extraction (IE) comes in Named Entity Recognition (NER). NER is a subtask of IE that seeks to identify and classify the predefined categories of named entities in text documents. Considerable amount of work has been done on NER in recent years due to the increasing demand of automated texts and the wide availability of electronic corpora. While it is relatively easy and natural for a human reader to read and understand the context of a given article, getting a machine to understand and differentiate between words is a big challenge. For instance, the word ‘brown’ may refer to a person called Mr. Brown, or the colour of an item which is brown. Human readers can easily discern the meaning of the word by looking at the context of that particular sentence, but it would be almost impossible for a computer to interpret it without any additional information. To deal with the issue, researchers in NER field have proposed various rule-based systems (Wakao, Gaizauskas & Wilks, 1996; Krupka & Hausman, 1998; Maynard, Tablan, Ursu, Cunningham & Wilks, 2001). These systems are able to achieve high accuracy in recognition with the help of some lists of known named entities called gazetteers. The problem with rule-based approach is that it lacks the robustness and portability. It incurs steep maintenance cost especially when new rules need to be introduced for some new information or new domains. A better option is thus to use machine learning approach that is trainable and adaptable. Three wellknown machine learning approaches that have been used extensively in NER are Hidden Markov Model (HMM), Maximum Entropy Model (MEM) and Decision Tree. Many of the existing machine learning-based NER systems (Bikel, Schwartz & Weischedel, 1999; Zhou & Su, 2002; Borthwick, Sterling, Agichten & Grisham, 1998; Bender, Och & Ney, 2003; Chieu & Ng, 2002; Sekine, Grisham & Shinnou, 1998) are able to achieve near-human performance for named entity tagging, even though the overall performance is still about 2% short from the rule-based systems. There have also been many attempts to improve the performance of NER using a hybrid approach with the combination of handcrafted rules and statistical models (Mikheev, Moens & Grover, 1999; Srihari & Li, 2000; Seon, Ko, Kim & Seo, 2001). These systems can achieve relatively good performance in the targeted domains owing to the comprehensive handcrafted rules. Nevertheless, the portability problem still remains unsolved when it comes to dealing with NER in various domains. As such, this article presents a hybrid machine learning approach using MEM and HMM successively. The reason for using two statistical models in succession instead of one is due to the distinctive nature of the two models. HMM is able to achieve better performance than any other statistical models, and is generally regarded as the most successful one in machine learning approach. However, it suffers from sparseness problem, which means considerable amount of data is needed for it to achieve acceptable performance. On the other hand, MEM is able to maintain reasonable performance even when there is little data available for training purpose. The idea is therefore to walkthrough the testing corpus using MEM first in order to generate a temporary tagging result, while this procedure can be simultaneously used as a training process for HMM. During the second walkthrough, the corpus uses HMM for the final tagging. In this process, the temporary tagging result generated by MEM will be used as a reference for subsequent error checking and correction. In the case when there is little training data available, the final result can still be reliable based on the contribution of the initial MEM tagging result.

Named Entity Recognition (NER) is an Information Extraction (IE) building block. Though the information extraction process has been automated using various techniques to find and extract relevant information from unstructured documents, the discovery of targeted knowledge still poses many research difficulties because of Web data's variability and lack of structure. NER, a subtask of IE, came to exist to smooth such difficulty. It deals with finding the proper names (named entities), such as a person's name, country, location, organization, dates, and event in a document. It categorises them as predetermined labels, an initial step in IE tasks. This survey paper presents the roles and importance of NER to IE from the perspective of different algorithms and application area domains. Additionally, it summarizes how researchers implemented NER in particular application areas like finance, medicine, defense, business, food science, archeology, etc. It also outlines the three NER sequence labeling algorithms types: feature-based, neural network-based, and rule-based. Finally, the state-of-the-art and evaluation metrics of NER were presented.

In the medical field, a lot of unstructured information which is expressed by natural language exists in medical literature, technical documentation and medical records. IE (Information Extraction) as one of the most important research directions in natural language process aims to help humans extract concerned information automatically. NER (Named Entity Recognition) is one of the subsystems of IE and has direct influence on the quality of IE. Nowadays NER of medical field has not reached ideal precision largely due to the knowledge complexity in medical field. It is hard to describe the medical entity definitely in feature selection and current selected features are not rich and lack of semantic information. Besides that, different medical entities which have the similar characters more easily cause classification algorithm making wrong judgment. Combination multi classification algorithms such as SVM-KNN can overcome its own disadvantages and get higher performance. But current SVM-KNN classification algorithm may provide wrong categories results due to setting inappropriate K value and unbalanced examples distribution. In this paper, we introduce two-level modelling tool to help specialists to build semantic models and select features from them. We design and implement medical named entity recognition analysis engine based on UIMA framework and adopt improved SVM-KNN algorithm called EK-SVM-KNN (Extending K SVM-KNN) in classification. We collect experiment data from SLE(Systemic Lupus Erythematosus) clinical information system in Renji Hospital. We adopt 50 Pathology reports as training data and provide 1000 Pathology as test data. Experiment shows medical NER based on semantic model and improved SVM-KNN algorithm can enhance the quality of NER and we get the precision, recall rate and F value up to 86%.

Understanding the biology underpinning the natural regeneration of plant species in order to make plans for effective reforestation is a complex task. This can be aided by providing access to databases that contain long-term and wide-scale geographical information on species distribution, habitat, and reproduction. Although there exists widely-used biodiversity databases that contain structured information on species and their occurrences, such as the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA), the bulk of knowledge about biodiversity still remains embedded in textual documents. Unstructured information can be made more accessible and useful for large-scale studies if there are tools and services that automatically extract meaningful information from text and store it in structured formats, e.g., open biodiversity databases, ready to be consumed for analysis (Thessen et al. 2022). We aim to enrich biodiversity occurrence databases with information on species reproductive condition and habitat, derived from text. In previous work, we developed unsupervised approaches to extract related habitats and their locations, and related reproductive condition and temporal expressions (Gabud and Batista-Navarro 2018). We built a new unsupervised hybrid approach for relation extraction (RE), which is a combination of classical rule-based pattern-matching methods and transformer-based language models that framed our RE task as a natural language inference (NLI) task. Using our hybrid approach for RE, we were able to extract related biodiversity entities from text even without a large training dataset. In this work, we implement an information extraction (IE) pipeline comprised of a named entity recognition (NER) tool and our hybrid relation extraction (RE) tool. The NER tool is a transformer-based language model that was pretrained on scientific text and then fine-tuned using COPIOUS (Conserving Philippine Biodiversity by Understanding big data; Nguyen et al. 2019), a gold standard corpus containing named entities relevant to species occurrence. We applied the NER tool to automatically annotate geographical location, temporal expression and habitat information contained within sentences. A dictionary-based approach is then used to identify mentions of reproductive conditions in text (e.g., phrases such as "fruited heavily" and "mass flowering"). We then use our hybrid RE tool to extract reproductive condition - temporal expression and habitat - geographical location entity pairs. We test our IE pipeline on the forestry compendium available in the CABI Digital Library (Centre for Agricultural and Biosciences International), and show that our work enables the enrichment of descriptive information on reproductive and habitat conditions of species. This work is a step towards enhancing a biodiversity database with the inclusion of habitat and reproductive condition information extracted from text.

Crime base: Towards building a knowledge base for crime entities and their relationships from online news papers

Named Entity Recognition is a subtask in Information Extraction. In Named Entity Recognition (NER) we try to identify each of the words provided into some categories predefined by us. These categories or classes can be organization, name, time, place etc. Named Entity Recognition is a part of Natural Language Processing which is aims at making human and computer interactions more meaningful and efficient. NLP is nowadays being effectively being used in the area of automatic text summarization, cross language information retrieval, speech recognition and query named entity recognition. This paper focuses on Named entity recognition, how it is performed and a how apply NER to web search queries so that correct and more user personalized results are displayed by the web search engines..

Although Large language Model (LLM)-powered information extraction (IE) systems have shown impressive capabilities, current fine-tuning paradigms face two major limitations: high training costs and difficulties in aligning with LLM preferences. To address these issues, we propose a novel universal IE paradigm—the Self-Correcting Iterative Refinement (SCIR) framework—along with a Multi-task Bilingual (Chinese-English) Self-Correcting (MBSC) dataset containing over 100,000 entries. The SCIR framework achieves plug-and-play compatibility with existing LLMs and IE systems through its Dual-Path Self-Correcting module and feedback-driven optimization, thereby significantly reducing training costs. Concurrently, the MBSC dataset tackles the challenge of preference alignment by indirectly distilling GPT-4's capabilities into IE result detection models. Experimental results demonstrate that SCIR outperforms state-of-the-art IE methods across three key tasks— named entity recognition, relation extraction, and event extraction—achieving a 5.27 percent average improvement in span-based Micro-F1 while reducing training costs by 87 percent compared to baseline approaches. These advancements not only enhance the flexibility and accuracy of IE systems but also pave the way for lightweight and efficient IE paradigms.

In this paper we present JUDIE (Joint Unsupervised Structure Discovery and Information Extraction), a new method for automatically extracting semi-structured data records in the form of continuous text (e.g., bibliographic citations, postal addresses, classified ads, etc.) and having no explicit delimiters between them. While in state-of-the-art Information Extraction methods the structure of the data records is manually supplied the by user as a training step, JUDIE is capable of detecting the structure of each individual record being extracted without any user assistance. This is accomplished by a novel Structure Discovery algorithm that, given a sequence of labels representing attributes assigned to potential values, groups these labels into individual records by looking for frequent patterns of label repetitions among the given sequence. We also show how to integrate this algorithm in the information extraction process by means of successive refinement steps that alternate information extraction and structure discovery. Through an extensively experimental evaluation with different datasets in distinct domains, we compare JUDIE with state-of-the-art information extraction methods and conclude that, even without any user intervention, it is able to achieve high quality results on the tasks of discovering the structure of the records and extracting information from them.

Three common approaches for deriving or predicting instantiated relations are information extraction, deductive reasoning and machine learning. Information extraction uses subsymbolic unstructured sensory information, e.g. in form of texts or images, and extracts statements using various methods ranging from simple classifiers to the most sophisticated NLP approaches. Deductive reasoning is based on a symbolic representation and derives new statements from logical axioms. Finally, machine learning can both support information extraction by deriving symbolic representations from sensory data, e.g., via classification, and can support deductive reasoning by exploiting regularities in structured data. In this paper we combine all three methods to exploit the available information in a modular way, by which we mean that each approach, i.e., information extraction, deductive reasoning, machine learning, can be optimized independently to be combined in an overall system. We validate our model using data from the YAGO2 ontology, and from Linked Life Data and Bio2RDF, all of which are part of the Linked Open Data (LOD) cloud.

This chapter explores the transformative impact of Named Entity Recognition (NER) and Information Extraction (IE) on business decision-making, highlighting their pivotal role in converting unstructured data into structured, actionable insights. The integration of NER and IE into business intelligence frameworks enhances competitive advantage, enables real-time decision support, and fosters deeper customer engagement through advanced sentiment analysis. The chapter also examines the significant applications of these technologies across key sectors such as healthcare, finance, and marketing, emphasizing their contribution to predictive analytics and operational efficiency. Furthermore, it addresses challenges such as scalability and data privacy concerns in large-scale implementations. By delving into the methodologies and best practices for deploying NER and IE, this chapter provides a comprehensive understanding of how these technologies are reshaping data-driven strategies and enabling businesses to thrive in an increasingly data-centric world. Key concepts covered include: Natural Language Processing (NLP), machine learning, data extraction, sentiment analysis, real-time analytics, and business intelligence.

In this paper we describe a rule-based formalism for the analysis and labelling of texts segments. The rules are contextual rewriting rules with a restricted form of negation. They allow to underspecify text segments not considered relevant to a given task and to base decisions upon context. A parser for these rules is presented and consistence and completeness issues are discussed. Some results of an implementation of this parser with a set of rules oriented to the segmentation of texts in propositions are shown.