Named Entity Recognition (NER) is the process of identifying specific groups of words which share common semantic characteristics. Consider organization names for instance. They may show superficial differences in the way they look but all convey the same type of information. They also usually appear in comparable contexts. Same with person names, locations or expressions of time. Finding this type of information helps improving search results and document classification. It is also the corner stone for other Natural Language Processing (NLP) techniques such as Named Entity Disambiguation, which is the task of linking different occurrences to the same entity. For instance, "Mr Page", "L. Page" or "Page, Larry" would all point to the same canonical entity, "Larry Page". NER is sometimes considered a solved problem. At the very least, we can say that indeed, well-trained systems reach extremely high scores, almost comparable to human performance. Techniques evolved from rule-based systems, to statistical models such as CRFs or Maximum Entropy to Neural Nets, consistently bridging the gap with human efficiency. Why bother writing a blog entry about it then?
NER has become more of a data problem than an algorithm problem
Training an NER engine requires appropriate and annotated data. Appropriate refers to the domain of the documents. Law firms produce different documents than medical firms and these are also different from newspaper articles. The training corpus needs to match the type of document you want to analyse. Annotated refers to the fact that named entities must be identified in the training set, and these annotations must be reliable to produce a well-performing engine. Producing training sets is the costly part of creating an NER engine. It involves a considerable amount of human labour. We should always keep in mind that many machine learning techniques ultimately rely on data composed by humans, be it image recognition, machine translation or of course, NER. The academic world produces such training sets for evaluation campaigns, but these are not always relevant to your domain or language. These datasets are often reused for several years while the algorithms get more efficient. Therefore, we are facing a data problem rather than an algorithm problem. Algorithms will evolve and improve overtime, but they will always rely on high-quality training material that is difficult and costly to produce.
