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...

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...

Word Sense Disambiguation(WSD) is the ability to identify best sense of a word in a particular context, when the word has multiple meanings. It can be considered as a classification problem: Given a word and it's meanings(senses), classify the word in one of it's sense class based on evidence from the context and external knowledge sources. For example, consider the following two sentences: a) The workers at the plant were overworked. b) The gardener was watering the plant. In first sentence, the word 'plant' refers to the industrial plant whereas in second one, it refers to a tree.   Word Sense Disambiguation System. WSD is an important part of  many applications such as Machine Translation , Information Retrieval , Information Extraction , Content Analysis , Word Processing (Spelling Correction) ,  Semantic Web etc . It  can help in improving the relevance of search engines, anaphora resolution , coherence, inference etc.  ...