scholarly journals An Improved Word Representation for Deep Learning Based NER in Indian Languages

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 186 ◽  
Author(s):  
Ajees A P ◽  
Manju K ◽  
Sumam Mary Idicula
Keyword(s):  
Deep Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Indian Languages ◽  
Named Entity ◽  
Text Document ◽  
Learning Techniques ◽  
Word Representation

Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications like information retrieval, question answering, machine translation and so forth. Resolving the ambiguities of lexical items involved in a text document is a challenging task. NER in Indian languages is always a complex task due to their morphological richness and agglutinative nature. Even though different solutions were proposed for NER, it is still an unsolved problem. Traditional approaches to Named Entity Recognition were based on the application of hand-crafted features to classical machine learning techniques such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Field (CRF) and so forth. But the introduction of deep learning techniques to the NER problem changed the scenario, where the state of art results have been achieved using deep learning architectures. In this paper, we address the problem of effective word representation for NER in Indian languages by capturing the syntactic, semantic and morphological information. We propose a deep learning based entity extraction system for Indian languages using a novel combined word representation, including character-level, word-level and affix-level embeddings. We have used ‘ARNEKT-IECSIL 2018’ shared data for training and testing. Our results highlight the improvement that we obtained over the existing pre-trained word representations.

scholarly journals A Kernel-Based Approach for Biomedical Named Entity Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rakesh Patra ◽  
Sujan Kumar Saha
Keyword(s):  
Kernel Function ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Kernel Functions ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Named Entity ◽  
Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

Chemical Named Entity Recognition Using Deep Learning Techniques

2021 ◽  
pp. 59-73
Author(s):  
Hema R. ◽  
Ajantha Devi
Keyword(s):  
Deep Learning ◽  
Chemical Elements ◽  
Data Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Related Data ◽  
Named Entity ◽  
Chemical Structures ◽  
Learning Techniques ◽  
Chemical Named Entity Recognition

Chemical entities can be represented in different forms like chemical names, chemical formulae, and chemical structures. Because of the different classification frameworks for chemical names, the task of distinguishing proof or extraction of chemical elements with less ambiguous is considered a major test. Compound named entity recognition (NER) is the initial phase in any chemical-related data extraction strategy. The majority of the chemical NER is done utilizing dictionary-based, rule-based, and machine learning procedures. Recently, deep learning methods have evolved, and, in this chapter, the authors sketch out the various deep learning techniques applied for chemical NER. First, the authors introduced the fundamental concepts of chemical named entity recognition, the textual contents of chemical documents, and how these chemicals are represented in chemical literature. The chapter concludes with the strengths and weaknesses of the above methods and also the types of the chemical entities extracted.

scholarly journals Unsupervised Technique for Automatically Extracting Components of References

2020 ◽  
Vol 9 (1) ◽  
pp. 1000-1004
Keyword(s):  
Machine Learning ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scientific Publications ◽  
Standard Format ◽  
Named Entity ◽  
Learning Techniques ◽  
The Individual

The automatic extraction of bibliographic data remains a difficult task to the present day, when it's realized that the scientific publications are not in a standard format and every publications has its own template. There are many “regular expression” techniques and “supervised machine learning” techniques for extracting the entire details of the references mentioned within the bibliographic section. But there's no much difference within the percentage of their success. Our idea is to seek out whether unsupervised machine learning techniques can help us in increasing the share of success. This paper presents a technique for segregating and automatically extracting the individual components of references like Authors, Title of the references, publications details, etc., using “Unsupervised technique”, “Named-Entity recognition”(NER) technique and link these references to their corresponding full text article with the assistance of google

A survey of named entity recognition and classification

2007 ◽  
Vol 30 (1) ◽  
pp. 3-26 ◽  
Author(s):  
David Nadeau ◽  
Satoshi Sekine
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Exact Match ◽  
Named Entity ◽  
Word Level ◽  
Learning Techniques ◽  
Matching Techniques ◽  
Evaluation Techniques ◽  
Critical Aspects

This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.

Named Entity Recognition for Code Mixed Social Media Sentences

2021 ◽  
Vol 13 (2) ◽  
pp. 23-36
Author(s):  
Yashvardhan Sharma ◽  
Rupal Bhargava ◽  
Bapiraju Vamsi Tadikonda
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Short Term Memory ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Internet Applications ◽  
Named Entity ◽  
Code Mixing

With the increase of internet applications and social media platforms there has been an increase in the informal way of text communication. People belonging to different regions tend to mix their regional language with English on social media text. This has been the trend with many multilingual nations now and is commonly known as code mixing. In code mixing, multiple languages are used within a statement. The problem of named entity recognition (NER) is a well-researched topic in natural language processing (NLP), but the present NER systems tend to perform inefficiently on code-mixed text. This paper proposes three approaches to improve named entity recognizers for handling code-mixing. The first approach is based on machine learning techniques such as support vector machines and other tree-based classifiers. The second approach is based on neural networks and the third approach uses long short-term memory (LSTM) architecture to solve the problem.

Named Entity Recognition and Relation Extraction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-39
Author(s):  
Zara Nasar ◽  
Syed Waqar Jaffry ◽  
Muhammad Kamran Malik
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
The State ◽  
Entity Recognition ◽  
Joint Models ◽  
Named Entity ◽  
Textual Data ◽  
Benchmark Datasets

With the advent of Web 2.0, there exist many online platforms that result in massive textual-data production. With ever-increasing textual data at hand, it is of immense importance to extract information nuggets from this data. One approach towards effective harnessing of this unstructured textual data could be its transformation into structured text. Hence, this study aims to present an overview of approaches that can be applied to extract key insights from textual data in a structured way. For this, Named Entity Recognition and Relation Extraction are being majorly addressed in this review study. The former deals with identification of named entities, and the latter deals with problem of extracting relation between set of entities. This study covers early approaches as well as the developments made up till now using machine learning models. Survey findings conclude that deep-learning-based hybrid and joint models are currently governing the state-of-the-art. It is also observed that annotated benchmark datasets for various textual-data generators such as Twitter and other social forums are not available. This scarcity of dataset has resulted into relatively less progress in these domains. Additionally, the majority of the state-of-the-art techniques are offline and computationally expensive. Last, with increasing focus on deep-learning frameworks, there is need to understand and explain the under-going processes in deep architectures.

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