Named Entity Recognition Techniques Research Articles

Threat intelligence named entity recognition techniques based on few-shot learning

In today’s digital and internet era, threat intelligence analysis is of paramount importance to ensure network and information security. Named Entity Recognition (NER) is a fundamental task in natural language processing, aimed at identifying and extracting specific types of named entities from text, such as person names, locations, organization names, dates, times, currencies, and more. The quality of entities determines the effectiveness of upper-layer applications such as knowledge graphs. Recently, there has been a scarcity of training data in the threat intelligence field, and single models suffer from poor generalization ability. To address this, we propose a multi-view learning model, named the Few-shot Threat Intelligence Named Entity Recognition Model (FTM). We enhance the fusion method based on FTM, and further propose the FTM-GRU (Gate Recurrent Unit) model. The FTM model is based on the Tri-training algorithm to collaboratively train three few-shot NER models, leveraging the complementary nature of different model views to enable them to capture more threat intelligence domain knowledge at the coding level.FTM-GRU improves the fusion of multiple views. FTM-GRU uses the improved GRU model structure to control the memory and forgetting of view information, and introduces a relevance calculation unit to avoid redundancy of view information while highlighting important semantic features. We label and construct a few-shot Threat Intelligence Dataset (TID), and experiments on TID as well as the publicly available National Vulnerability Database (NVD) validate the effectiveness of our model for NER in the threat intelligence domain. Experimental results demonstrate that our proposed model achieves better recognition results in the task.

Survey on Context Based Identification of Customer Name Variations using ML Techniques

Abstract: The demand for automated validation of new customer company accounts, with a specific emphasis on resolving name variations within large databases, is steadily increasing. This surge in demand is a direct response to the significant scale at which data for new customers is being input into CRM systems, necessitating manual validation and correction processes. The primary objective of this survey paper is to identify machine learning methods capable of addressing the challenge of resolving name variations in extensive databases. To achieve this goal, we propose a multi-step approach. Firstly, we employ an approximate string matching algorithm with high computational speed to identify matches with a very high percentage of accuracy. In cases where such matches are not found in the system, we utilize named entity recognition techniques to annotate company names and their suffixes, such as INC, PVT, LTD, CORP, and CO, which may appear in various forms. To resolve abbreviation disambiguity, we explore the application of machine learning algorithms, including the naïve Bayes classifier, decision trees, and Support Vector Machines. In this survey paper, we conclude by presenting potential approximate string matching algorithms, a named entity recognition method, and a model for resolving abbreviation disambiguity. Our review not only provides a comprehensive overview of the current state of research in this area but also highlights gaps in the existing knowledge, offering valuable insights for future research and practical application.

ADAPTIVE DOMAIN-SPECIFIC NAMED ENTITY RECOGNITION METHOD WITH LIMITED DATA

The ever-evolving volume of digital information requires the development of innovative search strategies aimed at obtaining the necessary data efficiently and economically feasible. The urgency of the problem is emphasized by the growing complexity of information landscapes and the need for fast data extraction methodologies. In the field of natural language processing, named entity recognition (NER) is an essential task for extracting useful information from unstructured text input for further classification into predefined categories. Nevertheless, conventional methods frequently encounter difficulties when confronted with a limited amount of labeled data, posing challenges in real-world scenarios where obtaining substantial annotated datasets is problematic or costly. In order to address the problem of domain-specific NER with limited data, this work investigates NER techniques that can overcome these constraints by continuously learning from newly collected information on pre-trained models. Several techniques are also used for making the greatest use of the limited labeled data, such as using active learning, exploiting unlabeled data, and integrating domain knowledge. Using domain-specific datasets with different levels of annotation scarcity, the fine-tuning process of pre-trained models, such as transformer-based models (TRF) and Toc2Vec (token-to-vector) models is investigated. The results show that, in general, expanding the volume of training data enhances most models' performance for NER, particularly for models with sufficient learning ability. Depending on the model architecture and the complexity of the entity label being learned, the effect of more data on the model's performance can change. After increasing the training data by 20%, the LT2V model shows the most balanced growth in accuracy overall by 11% recognizing 73% of entities and processing speed. Meanwhile, with consistent processing speed and the greatest F1-score, the Transformer-based model (TRF) shows promise for effective learning with less data, achieving 74% successful prediction and a 7% increase in performance after expanding the training data to 81%. Our results pave the way for the creation of more resilient and efficient NER systems suited to specialized domains and further the field of domain-specific NER with sparse data. We also shed light on the relative merits of various NER models and training strategies, and offer perspectives for future research.

Spatiotemporal assessment of urban flooding hazard using social media: A case study of Zhengzhou ‘7·20’

Urban flooding is occurring more frequently and with more serious consequences. The importance of flood disaster management has been highlighted. Social media has become an important source of flood disaster information in flood hazard assessment, however, it suffers from a lack of geolocation information and many qualitative descriptions. To improve the application of social media in flood hazard assessment, this study proposed a flooding hazard assessment method. Machine learning (ML)-based named entity recognition (NER) technique was used to identify the location and time of inundation in tweets, which can complement and correct the spatiotemporal properties of tweets. The textual description of waterlogging level in social media was quantified by lexicon-based NER technique. Taking a heavy rainfall event that occurred in Zhengzhou in July 2021 as an example, the results show that social media-based flood hazard maps are effective in identifying waterlogging. This study provides a feasible way to obtain near real-time hazard information from social media to help rapid hazard response.

Automatic question generation using extended dependency parsing

<span>The importance of automatic question generation (AQG) systems in education is recognized for automating tasks and providing adaptive assessments. Recent research focuses on improving quality with advanced neural networks and machine learning techniques. However, selecting the appropriate target sentences and concepts remains challenging in AQG systems. To address this problem, the authors created a novel system that combined sentence structure analysis, dependency parsing approach, and named entity recognition techniques to select the relevant target words from the given sentence. The main goal of this paper is to develop an AQG system using syntactic and semantic sentence structure analysis. Evaluation using manual and automatic metrics shows good performance on simple and short sentences, with an overall score of 3.67 out of 5.0. As the field of AQG continues to evolve rapidly, future research should focus on developing more advanced models that can generate a wider range of questions, especially for complex sentence structures.</span>

Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers

Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on the availability of substantial volumes of annotated data, which is labor-intensive and inefficient. background: With increasing rates of polypharmacy, clinical drug toxicity has been closely monitored. Named Entity Recognition (NER) is a vital task for extracting valuable drug safety information from biomedical literature. Recently, many deep learning models in biomedical domain have made great progress for NER, especially pre-trained language models. However, these NER methods require large amounts of high-quality manually annotated data with named entities, which is labor intensive and inefficient. Methods: This study introduces a novel approach that diverges from the conventional reliance on manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings within the transformer encoder. Additionally, we formulate a composite loss function that integrates two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes demonstrate that our approach attains the targeted performance for NER tasks, solely relying on unlabeled data and named entity dictionaries. objective: To improve the performance of prediction Conclusion: Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A comprehensive analysis of the results validates the effectiveness of our approach in comparison to existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations among three identified entities is provided, offering a valuable reference for querying their interrelationships. method: In this work, instead of relying on the manually labeled data, a transformer-based Positive-Unlabeled Learning (PULearning) is proposed with adaptive learning and applied on the clinical cancer drug toxicity corpus. To improve the precision of prediction, relative position embeddings are used in transformer encoder. And then, a mixed loss is designed with two Kullback-Leibler (KL) regularizers for PULearning assumptions. Through adaptive sampling, our approach meets the expected performance for NER task only using unlabeled data and named entity dictionaries. result: The overall NER performance of our model obtains 0.819 of F1-score, while 0.841, 0.801 and 0.815 of F1-score on DRUG, CANCER and TOXI, respectively. other: None

Stylometric Fake News Detection Based on Natural Language Processing Using Named Entity Recognition: In-Domain and Cross-Domain Analysis

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

Using Data Augmentation and Bidirectional Encoder Representations from Transformers for Improving Punjabi Named Entity Recognition

Named entity recognition (NER) is a task of proper noun identification from natural language text and classification into various types such as location, person, and organization. Due to NER's applications in different natural language processing (NLP) tasks, numerous NER approaches and benchmark datasets have been proposed. However, developing NER techniques for low-resource languages is still limited due to the absence of substantial training datasets. Punjabi is a classic example of low resource language. Although various researchers have worked on Punjabi, they focused on the Gurmukhi script. To overcome the challenges in developing NER for the Shahmukhi script, we present an improved technique for Punjabi NER for the Shahmukhi script in this paper. We firstly extend the existing dataset by adding new NER classes by leveraging a novel Pool of Words data augmentation strategy. Our extended dataset has 11,31,509 tokens and 1,25,789 labeled entities with more named entities (NEs) than the older dataset. In the next step, we fine-tuned a transformer model known as Bidirectional Encoder Representations from Transformers (BERT) for the NER task. We performed experiments using the proposed approach on a new and older dataset version, showing that our method achieved competitive results.

CEO's functional experience and firm performance based on text mining.

The impact of a chief executive officer's (CEO's) functional experience on firm performance has gained the attention of many scholars. However, the measurement of functional experience is rarely disclosed in the public database. Few studies have been conducted on the comprehensive functional experience of CEOs. This paper used the upper echelons theory and obtained deep-level curricula vitae (CVs) data through the named entity recognition technique. First, we mined 15 consecutive years of CEOs' CVs from 2006 to 2020 from Chinese listed companies. Second, we extracted information throughout their careers and automatically classified their functional hierarchy. Finally, we constructed breadth (functional breadth: functional experience richness) and depth (functional depth: average tenure and the hierarchy of function) for empirical analysis. We found that a CEO's breadth is significantly negatively related to firm performance, and the quadratic term is significantly positive. A CEO's depth is significantly positively related to firm performance, and the quadratic term is significantly negative. The research results indicate a u-shaped relationship between a CEO's breadth and firm performance and an inverted u-shaped relationship between their depth and firm performance. The study's findings extend the literature on factors influencing firm performance and CEOs' functional experience. The study expands from the horizontal macro to the vertical micro level, providing new evidence to support the recruitment and selection of high-level corporate talent.

Nested Named Entity Recognition: A Survey

With the rapid development of text mining, many studies observe that text generally contains a variety of implicit information, and it is important to develop techniques for extracting such information. Named Entity Recognition (NER), the first step of information extraction, mainly identifies names of persons, locations, and organizations in text. Although existing neural-based NER approaches achieve great success in many language domains, most of them normally ignore the nested nature of named entities. Recently, diverse studies focus on the nested NER problem and yield state-of-the-art performance. This survey attempts to provide a comprehensive review on existing approaches for nested NER from the perspectives of the model architecture and the model property, which may help readers have a better understanding of the current research status and ideas. In this survey, we first introduce the background of nested NER, especially the differences between nested NER and traditional (i.e., flat) NER. We then review the existing nested NER approaches from 2002 to 2020 and mainly classify them into five categories according to the model architecture, including early rule-based, layered-based, region-based, hypergraph-based, and transition-based approaches. We also explore in greater depth the impact of key properties unique to nested NER approaches from the model property perspective, namely entity dependency, stage framework, error propagation, and tag scheme. Finally, we summarize the open challenges and point out a few possible future directions in this area. This survey would be useful for three kinds of readers: (i) Newcomers in the field who want to learn about NER, especially for nested NER. (ii) Researchers who want to clarify the relationship and advantages between flat NER and nested NER. (iii) Practitioners who just need to determine which NER technique (i.e., nested or not) works best in their applications.

Extraction and Analysis of Social Networks Data to Detect Traffic Accidents

Traffic accident detection is an important strategy governments can use to implement policies intended to reduce accidents. They usually use techniques such as image processing, RFID devices, among others. Social network mining has emerged as a low-cost alternative. However, social networks come with several challenges such as informal language and misspellings. This paper proposes a method to extract traffic accident data from Twitter in Spanish. The method consists of four phases. The first phase establishes the data collection mechanisms. The second consists of vectorially representing the messages and classifying them as accidents or non-accidents. The third phase uses named entity recognition techniques to detect the location. In the fourth phase, locations pass through a geocoder that returns their geographic coordinates. This method was applied to Bogota city and the data on Twitter were compared with the official traffic information source; comparisons showed some influence of Twitter on the commercial and industrial area of the city. The results reveal how effective the information on accidents reported on Twitter can be. It should therefore be considered as a source of information that may complement existing detection methods.

BERT based Named Entity Recognition for Automated Hadith Narrator Identification

Hadith serves as a second source of Islamic law for Muslims worldwide, especially in Indonesia, which has the world's most significant Muslim population of 228.68 million people. However, not all Hadith texts have been certified and approved for use, and several falsified Hadiths make it challenging to distinguish between authentic and fabricated Hadiths. In terms of Hadith science, determining the authenticity of a Hadith can be accomplished by examining its Sanad and Matn. Sanad is an essential aspect of the Hadith because it indicates the chain of the Narrator who transmits the Hadith. The research reported in this paper provides an advanced Natural Language Processing (NLP) technique for identifying and authenticating the Narrator of Hadith as a part of Sanad, utilizing Named Entity Recognition (NER) to address the necessity of authenticating the Hadith. The NER technique described in the research adds an extra feed-forward classifier to the last layer of the pre-trained BERT model. In the testing process using Cahya/bert-base-indonesian-1.5G, the proposed solution received an overall F1-score of 99.63 percent. On the Hadith Narrator Identification using other Hadith passages, the final examination yielded a 98.27 percent F1-score.

Automatic Text Summarization using Document Clustering Named Entity Recognition

Due to the rapid development of internet technology, social media and popular research article databases have generated many open text information. This large amount of textual information leads to 'Big Data'. Textual information can be recorded repeatedly about an event or topic on different websites. Text summarization (TS) is an emerging research field that helps to produce summary from a single or multiple documents. The redundant information in the documents is difficult, hence part or all of the sentences may be omitted without changing the gist of the document. TS can be organized as an exposition to collect accents from its special position, rather than being semantic in nature. Non-ASCII characters and pronunciation, including tokenizing and lemmatization are involved in generating a summary. This research work has proposed an Entity Aware Text Summarization using Document Clustering (EASDC) technique to extract summary from multi-documents. Named Entity Recognition (NER) has a vital part in the proposed work. The topics and key terms are identified using the NER technique. Extracted entities are ranked with Zipf’s law and sentence clusters are formed using k-means clustering. Cosine similarity-based technique is used to eliminate the similar sentences from multi-documents and produce unique summary. The proposed EASDC technique is evaluated using CNN dataset and it shown an improvement of 1.6 percentage when compared with the baseline methods of Textrank and Lexrank.

Recurrent Neural Network-Based Model for Named Entity Recognition with Improved Word Embeddings

Extraction of meaningful information from a huge amount of data available on the web is a quite challenging task. The challenges faced in information extraction can be overcome with the help of an efficient named entity recognition (NER) system. Named entities are the proper names that play an important role in searching important information of interest. In this study, an efficient deep learning-based NER technique has been proposed which recognizes the named entities belonging to the general domain from Hindi, Punjabi, and bilingual Hindi and Punjabi text. An important variant of recurrent neural network, namely bidirectional long short-term memory-based model using improved word embeddings has been developed. Improved word embeddings are the combination of character convolutional neural network embeddings and part of speech embeddings. The main findings of the study include the development of a NER system that can extract named entities not only from Hindi and Punjabi datasets individually but also from mixed Hindi and Punjabi text. Besides, improved word embeddings are the combination of character-level features and word-level features which we find as the novel work as per our knowledge. Improved word embeddings are found to be effective in achieving better results than the results obtained by earlier NER models with deep feature extraction tasks.

Improving deep learning method for biomedical named entity recognition by using entity definition information

BackgroundBiomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information.Material and methodWe investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score.ResultsEntity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER.ConclusionOur entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.

Aspect-Based and Multi-Level Sentiment Information Applying Contrast Dictionary

The customer feedbacks provide alternative and important sources to discover knowledge supporting the marketers and customers to make better decisions. However, the manual process to extract useful information depends on domain experts. This paper focuses on improving the performance of the automatic sentiment information extraction from customer feedbacks. The article proposes a new extraction method that consider multiple dimensions of feedback information, aspect, word, contrast, sentence or phrase, and document levels. The aspect-based sentiment extraction uses a named entity recognition technique to extract the desired aspects of a target product. The aspect-based sentiment combines with sentiment information from multiple levels of feedback contexts resulting in the fused sentiment information improves the extraction performance. We validate the effectiveness by measuring the accuracy of the sentiment and aspect recognition methods comparing with SentiStrength and Word-Count. This information gives some insights on customer satisfaction and can be applied in an alarming tool.

A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.

Tracking sentiment towards news entities from Arabic news on social media

The tracking sentiment of the news entities over time provides important information to governments and enterprises during the decision-making process. Recently, it has attracted the attention of the research community as well due to its popularity in many applications including; tracking news about elections, e-commerce, and e-governance. However, most of the work is focused on English whereas limited contributions have been done for Arabic. Moreover, there are no annotated corpora in the Arabic news domain that can be used to perform the sentiment tracking task. In this research, we present an Arabic news corpus and its associated sentiment tracking system to monitor the sentiments towards news entities in the Arab world. Sentiment classification and Named Entity Recognition techniques are used to prepare the corpus for the tracking task. A sample dataset containing 7200 tweets was manually annotated to be used in building multiple classifiers and annotate more than 2.3M tweets using the semi-supervised technique. The results of sentiment classification by using different machine learning classifiers and internal testing set show that semi-automatically annotated dataset outperforms the manually annotated dataset by 23% and 16% on two-way and three-way classification respectively using F1-score. The tracking results illustrate that over time the sentiment tracking performs well at discovering the most popular entities, from social media and, tracking their shifts in different Arab regions. It can be used to detect the possible reasons for sentiment change over time and, to predict the future sentiment of the news entities.

Semi-supervised deep learning\xa0based named entity recognition model to parse education section of resumes

A job seeker’s resume contains several sections, including educational qualifications. Educational qualifications capture the knowledge and skills relevant to the job. Machine processing of the education sections of resumes has been a difficult task. In this paper, we attempt to identify educational institutions’ names and degrees from a resume’s education section. Usually, a significant amount of annotated data is required for neural network-based named entity recognition techniques. A semi-supervised approach is used to overcome the lack of large annotated data. We trained a deep neural network model on an initial (seed) set of resume education sections. This model is used to predict entities of unlabeled education sections and is rectified using a correction module. The education sections containing the rectified entities are augmented to the seed set. The updated seed set is used for retraining, leading to better accuracy than the previously trained model. This way, it can provide a high overall accuracy without the need of large annotated data. Our model has achieved an accuracy of 92.06% on the named entity recognition task.

Named Entity Recognition and Classification for Punjabi Shahmukhi

Named entity recognition (NER) refers to the identification of proper nouns from natural language text and classifying them into named entity types, such as person, location, and organization. Due to the widespread applications of NER, numerous NER techniques and benchmark datasets have been developed for both Western and Asian languages. Even though Shahmukhi script of the Punjabi language has been used by nearly three fourths of the Punjabi speakers worldwide, Gurmukhi has been the main focus of research activities. Specifically, a benchmark NER corpus for Shahmukhi is non-existent, which has thwarted the commencement of NER research for the Shahmukhi script. To this end, this article presents the development and specifications of the first-ever NER corpus for Shahmukhi. The newly developed corpus is composed of 318,275 tokens and 16,300 named entities, including 11,147 persons, 3,140 locations, and 2,013 organizations. To establish the strength of our corpus, we have compared the specifications of our corpus with its Gurmukhi counterparts. Furthermore, we have demonstrated the usability of our corpus using five supervised learning techniques, including two state-of-the-art deep learning techniques. The results are compared, and valuable insights about the behaviors of the most effective technique are discussed.