Named Entities Recognition (NER)

Named Entity Recognition (NER) is a field of natural language processing (NLP) that identifies and defines certain words or phrases within a sentence or a document. It is used to classify and differentiate items from one another, such as people, locations, organizations, dates, etc. The goal of NER is to accurately determine whether a word stands for an item of interest. NER is commonly used in search engines to improve accuracy and relevancy of search results by focusing more specifically on the parameters set by the user. It can also be used as part of automatic text summarization techniques, allowing only important keywords or phrases to be included in the summary.

In practical usage, many use NER alongside other Natural Language Processing (NLP) techniques such as Lexical Analysis, Part-of-Speech Tagging, Entity Linking and Entity Disambiguation. For example, NER can be used with Parts-of-Speech tagging methods to identify adjectives like names or organizations (e.g., “Apple computer” ). Thereafter entity linking or disambiguation can used to further refine the recognition process.

When training models for Named Entity Recognition it’s important that enough labeled data sets are available in order to train the model effectively. The model will need plenty of labeled examples so it can better recognize these entities when presented with new data points. It is also important that various forms of entities are explored (e.g., singular vs plural forms). Furthermore for real world applications transfer learning may be employed on large pre-trained datasets like BERT which have been trained on a large corpus of domain specific data points. Such large datasets are often useful when dealing with rare entities which do not have labeled data readily available in smaller datasets

There are a variety of open-source tools available for Named Entity Recognition tasks. Some of the most commonly used NER libraries, research tools, and NLP frameworks include: Stanford NLP, SpaCy, Apache OpenNLP, Polyglot Framework and Open Source Speech Recognition (OSS). Each of these frameworks offer different advantages based on the needs of the project. For example, Stanford’s CoreNLP provides an array of annotation options to customize Name Entity recognition and includes many built-in annotations and modeling options. SpaCy is another popular library that offers pre-trained models for named entity recognition that can be easily integrated into application development environments. Apache OpenNLP provides highly efficient algorithms and libraries for natural language processing that can be used to identify and extract features from text. Finally, Polyglot Framework is a great platform if you are looking for a more low-level approach to natural language processing and entity recognition. With all these options it should be easy to find the right library or framework for every specific Named Entity Recognition project.