Amalgam

Topic Modelling is essentially a method to find the topics from a document. These topics are set of words which best describe the document. It helps us to understand and organize large amount of information. In the era of exponentially increasing data, it is very difficult to understand and summarize the large collections of unstructured textual documents. Topic Modelling not only helps to understand the semantics of documents, it also helps to annotate them. It helps to recover hidden and recurring patterns in texts and can be considered as a text mining tool. There are multiple Topic models like Latent Dirichlet Allocation, TextRank and Probabilistic Latent Semantic Analysis etc. Latent Dirichlet Allocation (LDA) LDA is a statistical model. Intuition behind LDA is that every document contains some topics and every word in the document is attributable to one of the topics. It is different from PLSA in the idea that every document is a mixture of small number of topic...

The recent trends in NLP has shown great interest in understanding text to perform various tasks. Understanding text to perform mathematical reasoning have focused on automatically solving school level math word problems. Advancement in this area has great potential to be used as automatic tutoring service for school students. This blog focuses on a web based tool named ILLINOIS MATH SOLVER that supports performing mathematical reasoning [1]. The solver can answer a wide range of mathematics questions, ranging from operation questions like “What is the result when 6 is divided by the sum of 7 and 5 ?”  to elementary school level math word problems, like “I bought 6 apples. I ate 3 of them. How many do I have left ?”.  ILLINOIS MATH SOLVER provides an easy way to test the robustness of the system, and a tool for crowd based data acquisition. Fig. Screenshot of Illinois Math Solver Working Description The whole system comprises of two different modules, firstly a Conte...