The importance of fine grained named entity recognition

Name entity recognition is usually viewed as a low level NLP task but could be crucial to other tasks such as named entity disambiguation and linking. It is also relevant for information retrieval and question and answering applications. Standard named entity recognition classes are usually person, location and miscellaneous. I used the AllenNLP demo application to run a quick NER test for the Hacksaw ridge storyline. The text was extracted from the IMDB website and the below image indicates the entities. Previous research led to the identification of three core classes – person, location and organisation.  During the Computational Natural Language Learning conference of 2003, a miscellaneous type was then added to the mix

The below reveals the four main entity classes or the non-fine grained, All four (person, organisation, location and miscellaneous) entity tags are highlighted. Desmond T. Doss is the name of the star character in the story and it is accurately identifies him as a person. When his surname was mentioned (Doss’s), it also has the accurate personal tag.  The miscellaneous tag was used for events like the ‘Battle of Okinawa’ and a thing ‘Congressional medal of honor.’

fine -grained named entity recognition

Whilst the stas Further research also introduced geopolitical entities such as weapons vehicles and facilities.  These were all contained in the article, “An empirical study on fine-grained named entity recognition”, and the authors further revealed that the apparent challenges of developing a fine-grained entity recognizer are because of the selection  of the tag set, creation of training data and the creation of a fast and accurate multi-class labelling algorithm.

With the benefit of AllenNLP, a fine-grained entity recognition was ran. The miscellaneous tag used for ‘the Congressional Medal of Honor’ phrase in  a standard NER (Named Entity recognition) task is different in a fine-grained NER. ‘Work of art’ is revealed as an entity tag and adds more meaning than a miscellaneous tag.

fine-grained named entity recognition

Previous research on fine-grained named entity recognition has led to more in-depth tags. In these works, the main tags are divided into sub-tags to generate more meaning to the entities. For example, the ‘Person’ entity is broken down to sub-categories such as actor, architect, artist, athlete, author, coach, director, doctor, engineer, monarch, politician, religious leader, soldier and terrorist.

The popular python NLP library SpaCy, also has a named entity recognition feature and some of the tags it supports are person, NORP (Nationalities or political or religious group), FAC (Building, airports, highways, bridges e.t.c), GPE (Countries, cities, states) and a lot more entities. One can easily state that a fine-grained named entity recognition application or library could be instrumental in narrative intelligence and relational reasoning. As the more detailed or fine-grained meaning the entities is a narrative can be expressed, the more enriched the story becomes and its ability to embody a string relational reasoning.

 

Relational reasoning as the basis for human intelligence

A blog and research paper from Google Deepmind brought our attention to the concept of relational reasoning. As humans we have the innate ability to connect the dots or plot a narrative from piece of information to make a decision either to run a search, make a purchase or predict the outcome of a movie. Artificial agents are yet to attain the creative human ability to connect entities together via a narrative exercise that leads to human action. A few days ago my wife told me an interesting story. She ran into a friend at the entrance of a shop, he’s just grabbed himself a drink and some popcorn. She asked him where he was headed and he said to West India Quay. It is a place in east London which boasts of a handful of restaurants, bars and a cinema. She pieced together the the popcorn, drinks and West Indian Quay and asked him if he was headed to the cinema? He was quite surprised and affirmed he was headed to the cinema. She then predicted or stated that he owns a monthly Cineworld membership. He was quite shocked and nodded to having a monthly Cineworld membership. She told me, her experience of buying popcorn, drink and preferring the West India Quay Cineworld with her Meetup movie mates, assisted her in creating a relationship and narrative from the little information she received to correctly predict the intention of her friend. This is relational reasoning at work as the deepmind team clearly mentioned “ We carve our world into relations between things. And we understand how the world works through our capacity to draw logical conclusions about how these different things – such as physical objects, sentences, or even abstract ideas – are related to one another.”

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