I was trying to absolve one name entity resolution problem.Suppose, I have two sentences,Cameron is the PM of England . He decided to step down .Cameron was trying to keep England in EU . Mr. Cameron could not do it .These two sentences have two different properties, first contains a pronoun, and second one alias against Cameron. My objective is to determine the entities(NE or otherwise) and link the entities appropriately, considering pronoun and alias are entities.I have divided the task in two phrases.In the first I am using one entity extraction algorithm, and in the second, trying to link them.I am trying to use Supervised Learning algorithm for both of them.I used one Hidden Markov Model(HMM) to extract the entities.I had given user defined training set and extracted entities as,Cameron/PERS is/NA the/NA PM/EXE of/NA England/LOC ./NA He/PRN decided/NA to/NA step/NA down/NA ./NAand,Cameron/PERS was/NA trying/NA to/NA keep/NA England/LOC in EU/ORG ./NA Mr./TIT Cameron/PERS could/NA not/NA do/NA it/NA ./NAI am using one standard Naive Bayes(NB) classifier to classify these two sentences belonging to two groups.The first one being 'PRN-STRING' and second one 'ALIAS'.I am using a new training set in NB classifier, and classifying the extracted sentences.The first is being labelled as 'PRN-STRING' and second 'ALIAS'.My questions are(i) Am I doing right? As much I read about entity linking I am going right, my friends and seniors also say so.(ii) Classification and extraction tasks are independent, but am I right to combine in the same code?In my code I am tagging with HMM and after some processing of the output, pushing as input to the classifier.NB: EXE-> It is a User defined tag as Executive.
Name Entity Linking with Naive Bayes Classifier
machine learning;nlp;multiclass classification;naive bayes classifier