Stokastik

Machine Learning, AI and Programming

Tag: Conditional Random Fields

Attribute Extraction from E-Commerce Product Description

In this post we are going to look into how one can use product title and description on e-commerce websites to extract different attributes of the product. This is a very fundamental problem in e-commerce which has widespread implications for Product Search (search filters), Product Matching (matching same items from different sellers), Product Grouping (grouping items by variants such as size and color), Product Graph (relationship between products based on […]

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Building an Incremental Named Entity Recognizer System

In the last post, we saw how to train a system to identify Part Of Speech tags for words in sentences. In essence we found out that discriminative models such as Neural Networks and Conditional Random Fields, outperforms other methods by 5-6% in prediction accuracy. In this post, we will look at another common problem in Natural Language Processing, known as the Named Entity Recognition (NER in short). The problem […]

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Understanding Conditional Random Fields

Given a sequence of observations, many machine learning tasks require us to label each observation in the sequence with a corresponding class (or named entity) such that the overall likelihood of the labelling is maximized. For example, given a english sentence, i.e. a sequence of words, label each word with a Part-Of-Speech tag, such that the combined POS tag of the sentence is optimum. "Machine Learning is a field of […]

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