Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naive-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

Information extraction from scientific articles: a survey

Chinese named entity recognition: The state of the art

Named entity recognition (NER) is a natural language processing tool for information extraction from unstructured text data such as e-mails, newspapers, blogs, etc. NER is the process of identifyin...

Named Entity Recognition Approaches and Their Comparison for Custom NER Model

Named Entity Recognition using Conditional Random Fields

Named Entity Recognition (NER) is sub task of Information Extraction that includes identification of named entities and classification of them into named entity classes such as person, location and organization etc. NER can be used to preprocess textual information and convert it into structured form that can be useful for Information Retrieval, Machine Translation, Question Answering System and Text Summarization. This paper presents a survey regarding NER research done for various Indian and non Indian languages. The study and observations related to approaches, techniques and features required to implement NER for various languages especially for Indian languages is reported. General Terms NER (Named Entity Recognition), HMM (Hidden Markov Model), CRF (Conditional Random Fields), SVM (Support Vector Machine)

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Survey of Named Entity Recognition Systems with respect to Indian and Foreign Languages

Information Extraction (IE) is a sub discipline of Artificial Intelligence. IE identifies information in unstructured information source that adheres to predefined semantics i.e. people, location etc. Recognition of named entities (NEs) from computer readable natural language text is significant task of IE and natural language processing (NLP). Named entity (NE) extraction is important step for processing unstructured content. Unstructured data is computationally opaque. Computers require computationally transparent data for processing. IE adds meaning to raw data so that it can be easily processed by computers. There are various different approaches that are applied for extraction of entities from text. This paper elaborates need of NE recognition for Marathi and discusses issues and challenges involved in NE recognition tasks for Marathi language. It also explores various methods and techniques that are useful for creation of learning resources and lexicons that are important for extraction of NEs from natural language unstructured text.

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Issues and challenges in marathi named entity recognition

Named Entity Recognition (NER) is the problem of identifying named entities in natural language text, classifying them into various classes and assigning the proper class tag to each word in its context. This paper describes a Named Entity Recognition system for Marathi using Hidden Markov Model (HMM). It addresses the problem of assigning the correct named entity class tag to each word using probabilistic model trained on a manually tagged corpus for the Marathi language. The most probable named entity tag is assigned to each word using the Viterbi algorithm. Proposed system reports an overall F1-score of 62.70% when no preprocessing was applied whereas it reports an overall F1-score of 77.79% when preprocessing was applied on the same data. Thus, the performance of the system is improved by 15% when linguistic knowledge is used to preprocess the test and training dataset.

HMM based Named Entity Recognition for inflectional language

This paper describes a named entity recognition system that combines hidden markov model, handcrafted rules, and gazetteers to recognize named entities in Marathi language. The objective of the system is to recognize twelve types of NEs from the Marathi text. Marathi is morphologically rich and inflectional language. The inflections in NEs are handled by using lemmatization. The difficulties of zero and poor probabilities caused due to the sparse data are handled using pseudo word replacement and smoothing techniques. Viterbi algorithm is used for decoding and word disambiguation. The performance of the system is improved using gazetteers and grammar rules.

Nita Patil

Papers

Survey of Named Entity Recognition Systems with respect to Indian and Foreign Languages

Named Entity Recognition using Conditional Random Fields

Issues and challenges in marathi named entity recognition

HMM based Named Entity Recognition for inflectional language

Hybrid Approach for Marathi Named Entity Recognition