An introduction to comparable entity mining at a structural level 

The comparing of entities is usually important in human decision making. 

We are constantly comparing entities daily from holiday destinations, new mobile phone and next family car. Comparing entities look at the relatedness of these objects or concepts. Relatedness does not look at only similarity (analogy) but other relational structures such as resistance (antithesis), discrepancy (anomaly) and incompatibility (antinomy). 

Comparative entity mining is crucial to any keyword, concept, content, product and marketing strategy. This is because no product exists in isolation. It is therefore important for businesses to place themselves in the shoes of potential customers and explore the alternative products that are vying for the same attention and mind space. Conducting this exercise will help brands position their products in a more compellingly through  through engaging branding and compelling storytelling. 

The field of biomedical informatics have employed comparative entity mining between genes and proteins. These comparisons have now extended to diseases. The comparisons in the biomedical field looks at functional similarities more than sequential similarities. This has inspired me to classify comparative entity mining to three stages: structural, functional and sequential. 

This blog will focus on the structural stage of comparing entities. We will be comparing bananas to plantains. When looking at an entity or a concept from the structural level we focus more on the size, colour, shape and physical properties. The internal properties that make up the product. 

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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.’

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.

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A Count-based and Predictive vector models in the Semantic Age

Computer applications such as search engines and dialogue systems usually process a large volume of text on a regular basis. These systems have progressed over the years due to advanced word embeddings, progressive research and modern machine learning libraries.  It is believed that audio and visual datasets have dense high-dimensional datasets encoded as vectors of a separate raw pixel. Text generated from natural language is usually understood to contain vectors that are sparse when compared to video and visuals.  

Vector space models (VSM) embed or represent words in a continuous vector space. In this respect, words that are semantically similar are plotted to nearby points.  As representing words as unique and distinct ids can usually lead to a sparsity of data. Going this route will require a large amount of data to be collected to effectively train statistical models. This is why vector representation of words is useful to address the problem of sparsity and enhance semantic interpretation.  I ran a search for romantic dinner on Google and one of the people also ask questions was ‘Where can I eat for anniversary?’ We can clearly see the semantic similarity of the term ‘romantic’ and ‘anniversary’ used within the context of food or dining. You would normally expect a distance between the vector representation of these words but from a contextual perspective, an anniversary is usually expected to be romantic as it will involve couples celebrating a milestone in their relationship or marriage. 

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