NERosetta – an Insight into Named Entity Tagging (original) (raw)

A STUDY ON THE APPROACHES OF DEVELOPING A NAMED ENTITY RECOGNITION TOOL

Named entity recognition (NER) is of vital importance in information extraction in natural language processing. Identifying the named entities in a piece of text and classifying them with proper tagging can help in getting a lot of information engraved in the particular text. The following paper presents brief details about the various approaches in developing a NER. Also an overview of the various models and learning methodologies used for the statistical approach is also provided. The various factors that need to be considered in developing this tool are also stated.

Named Entity Recognition and Resolution for Literary Studies

2014

This paper reports on the project Namescape: Mapping the Landscape of Names in Modern Dutch Literature, funded by CLARIN-NL. The background of the project is research in literary onomastics, the study of the usage and functions of proper names in literary (i.e. ctional) texts. The two main tasks for the project were to adapt existing Named Entity Recognition software to modern Dutch ction, and to perform Named Entity Resolution by linking the names to Wikipedia entries. For Named Entity Recognition, existing tools have been trained on literary texts and a new NE tagger has been developed. The standard list of name categories had to be extended, since the analysis of the usage of proper names in literature needs to distinguish e.g. between rst names and family names. The Named Entity Resolution task was done to explore the possibility of labeling the names in ction in another way, by categorizing a name as referring to a person or location that only exist in the story of a ctional wo...

Named Entity Recognition -- An Overview of Methods

[NOTE: The draft is imbalance in the depth in the coverage of methods. The slides at Dataday 2017 provides a short complement. If you are interested in particular topics, have critiques or comments, please feel free to email me at shrekwang@utexas.edu] A huge literature has been dedicated to NER. Here I give a bird-view overview of selected methods (supervised/semi-supervised/unsupervised + deep learning based methods), describing main ideas and sketch algorithms, with a focus on unsupervised NER, which I consider, from my work experience as a data scientist, the most useful in practice. In addition I propose an end-to-end unsupervised architecture, leveraging the combined force of traditional feature engineering and deep learning.

TNNT: The Named Entity Recognition Toolkit

ArXiv, 2021

Extraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is important in data analysis to make informed decisions. This paper presents TNNT, a toolkit that automates the extraction of categorised named entities from unstructured information encoded in source documents, using diverse state-of-the-art Natural Language Processing (NLP) tools and NER models. TNNT integrates 21 different NER models as part of a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as input and processes it based on the defined settings, applying the selected blocks of NER models to output the results. The toolkit generates all results with an integrated summary of the extracted entities, enabling enha...

A Concise Review of Named Entity Recognition System: Methods and Features

IOP Conference Series: Materials Science and Engineering

Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities ('atomic elements') in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.

The Tanl Named Entity Recognizer at Evalita 2009

Proc. of Workshop Evalita 2009, 2009

We describe the tagger present in the Tanl toolkit, which is a flexible and customizable tool for use in various tagging tasks, including POS tagging and SuperSense tagging. The tagger uses a variety of features, both local and global, which can be specified in a configuration file. The tagger is based on a Maximum Entropy classifier and uses dynamic programming to select accurate sequences of tags. We applied it to the NER tagging task in Evalita 2009, customizing the set of features to use and generating a set of dictionaries from the training corpus, that also provide additional features. The final accuracy is further improved by applying simple symbolic rules.

An Environment for Named Entity Recognition and Translation

2009

We present an environment for the recognition and translation of Named Entities (NEs). The environment consists of a new formalism for the Named Entity Recognition and Translation (NERT), a parsing mechanism that reads the rules, recognizes Named Entities in given texts and suggests their translation, as well as a set of tools for the evaluation. We suggest a method for the evaluation of (sets of) NERT rules that uses raw (not annotated) bilingual corpora.

On the Use of Parsing for Named Entity Recognition

2021

Parsing is a core natural language processing technique that can be used to obtain the structure underlying sentences in human languages. Named entity recognition (NER) is the task of identifying the entities that appear in a text. NER is a challenging natural language processing task that is essential to extract knowledge from texts in multiple domains, ranging from financial to medical. It is intuitive that the structure of a text can be helpful to determine whether or not a certain portion of it is an entity and if so, to establish its concrete limits. However, parsing has been a relatively little-used technique in NER systems, since most of them have chosen to consider shallow approaches to deal with text. In this work, we study the characteristics of NER, a task that is far from being solved despite its long history; we analyze the latest advances in parsing that make its use advisable in NER settings; we review the different approaches to NER that make use of syntactic informa...