NLP is also used for language translation and understanding. Machine learning models can be trained to translate text from one language to another, making it easier for people to communicate and understand each other. Additionally, NLP algorithms can process and understand the meaning of text, allow...
Many tasks in Natural Language Processing (NLP) can be formulated as the assignment of a label to an input. Often, the set of possible labels can be unmanageably large and even enumerating the set would be intractable. For example, the set of possible labels for machine translation might ...
Information Translation- to take a text as input and represent it in a structured form like a database entries. 3. Hard or still need lot of work Text Summarization- to take input as text document(s) and try to condense them into a summary. Machine dialog system- Example: User - I ne...
NLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems,code simplify inside Jupyter Notebooks 100%. Table of contents Objective Original implementations are quite complex and not really beginner friendly. So I tried to simplify most of it. Also, there are...
NLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. Table of contents Abstractive Summarization Chatbot Dependency Parser Entity Tagging Extractive Summarization Generator Language Detection Neural Machine Translation OC...
Recently, there has been growing interest in end-to-end deep neural networks for solving routing problems. However, such methods typically produce sequences of vertices, which make it difficult to apply them to general combinatorial optimization problems where the solution set consists of edges, as...
Blodgett SL, Barocas S, Daumé III H, Wallach H (2020) Language (technology) is power: a critical survey of “bias” in nlp. In: ACL Bo A, Peng S, Xinming T, Alimu N (2011) Spatio-temporal visualization system of news events based on gis. In: Communication software and networks ...
Existing annotations for a wide array of semantic NLP tasks are freely available. By leveraging existing semantic annotations already invested in by the community we can generate and label NLI pairs at little cost and create large NLI datasets to train data hungry models. Why These Semantic ...
State-of-the-art result for all Machine Learning Problems LAST UPDATE: 10th November 2017 NEWS: I am looking for a Collaborator esp who does research in NLP, Computer Vision and Reinforcement learning. If you are not a researcher, but you are willing, contact me. Email me:redditsota@gmail...
In this paper, we use the Transformer model[1]to solve arithmetic word problems as a particular case of machine translation from text to the language of arithmetic expressions. Transformers in various configurations have become a staple of NLP in the past two years. Past neural approaches did no...