Deep learning for natural language processing: advantages and challenges National Science Review
Event discovery in social media feeds (Benson et al.,2011) , using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. Here the speaker just initiates the process doesn’t take part in the language generation. It stores the history, structures the content that is potentially relevant and deploys a representation of what it knows.
With the increasing use of algorithms and artificial intelligence, businesses need to make sure that they are using NLP in an ethical and responsible way. Firstly, businesses need to ensure that their data is of high quality and is properly structured for NLP analysis. Poorly structured data can lead to inaccurate results and prevent the successful implementation of NLP. Cosine similarity is a method that can be used to resolve spelling mistakes for NLP tasks.
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Universal language model Bernardt argued that there are universal commonalities between languages that could be exploited by a universal language model. The challenge then is to obtain enough data and compute to train such a language model. This is closely related to recent efforts to train a cross-lingual Transformer language model and cross-lingual sentence embeddings. Embodied learning Stephan argued that we should use the information in available structured sources and knowledge bases such as Wikidata.
NLP has a wide range of real-world applications, such as virtual assistants, text summarization, sentiment analysis, and language translation. It is often possible to perform end-to-end training in deep learning for an application. This is because the model (deep neural network) offers rich representability and information in the data can be effectively ‘encoded’ in the model.
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For example – if any companies wants to take the user review of it existing product . POS tagging is one the common task which most of the NLP frameworks and API provide .This helps in identifying the Part of Speech into sentences . Usually you will not get any end application of this NLP feature but it is one of the most required tool in the mid of other big NLP process ( Pipeline) . Depending on the context, the same word changes according to the grammar rules of one or another language. To prepare a text as an input for processing or storing, it is needed to conduct text normalization. Optical character recognition (OCR) is the core technology for automatic text recognition.
These are the most common challenges that are faced in NLP that can be easily resolved. The main problem with a lot of models and the output they produce is down to the data inputted. If you focus on how you can improve the quality of your data using a Data-Centric AI mindset, you will start to see the accuracy in your models output increase. Word embedding creates a global glossary for itself — focusing on unique words without taking context into consideration. With this, the model can then learn about other words that also are found frequently or close to one another in a document.
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In general, NLP applications employ a set of POS tagging tools that assign a POS tag to each word or symbol in a given text. Subsequently, the position of each word in a sentence is determined by a dependency graph, generated in the same procedure. Those POS tags can be further processed to create meaningful single or compound vocabulary terms. The strength of statistical processing of text relies on the fact that language is inherently patterned on multiple levels.
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