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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...

Have u ever thought of a programming language which can be written in natural language. We came across different kind of applications which need graphs to be plotted and required data has to be stored and some complex actions have to be performed using Internet of things or on any other data. The above requirements can be accomplished using a programming Language which has to be written precisely following all the rules.     On the other hand, there is a method which can convert natural language into formal language. This can be done using semantic parsing. The ability of this parsing is limited and not as powerful as implementation through programming. Example for this is “Voxelurn”. Example for natural language programming    This concept is called “ naturalization ”. This bridges gap between natural language and core language. In any application development, we need to select a core language and we need to train the system with rules (conver...

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...

While making the best use of Big Data, data science aims at avoiding an information overload. For instance, search engines mine information based on a user query. Essentially, recommendation engines, such as Amazon, help narrow down on the appropriate information that the user may be seeking. Such recommendation systems are built based on broadly two types of filtering: collaborative filtering and content-based filtering . (Image source: https://www.themarketingtechnologist.co/building-a-recommendation-engine-for-geek-setting-up-the-prerequisites-13/ ) This blog focusses on the latter. Content-based recommenders make use of keywords associated with products or services and the user profile(including past likings by the user or current information/product/service being examined by the user). For instance, if a user follows news updates on Federer and Nadal, (s)he may be interested in news updates on these tennis players or on tennis. This is where techniques to process n...

Can you imagine, a life without eyesight? Can you feel the complete blackness surrounding you? I hope not.