NATURAL LANGUAGE PROCESSING USING MACHINE LEARNING TECHNIQUES

Abstract

The present invention relates to a method and system for enhancing Natural Language Processing (NLP) tasks using advanced machine learning techniques, particularly deep learning models such as transformers. By integrating preprocessing, feature extraction, and task-specific fine-tuning, the system improves the accuracy, scalability, and adaptability of NLP applications including text classification, sentiment analysis, named entity recognition, machine translation, and text summarization. The invention utilizes innovative approaches like transfer learning, reinforcement learning, and domain-specific embeddings to optimize performance across diverse domains and languages, enabling efficient processing of large-scale unstructured text data in real-time while reducing computational costs.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention addresses the challenges inherent in processing and understanding human language by leveraging advanced machine learning techniques, specifically deep learning models such as transformers. The system is designed to enhance the accuracy, scalability, and adaptability of NLP tasks, including but not limited to text classification, sentiment analysis, named entity recognition, machine translation, and text summarization.

The system operates in a multi-step pipeline that integrates several key components, starting with the preprocessing of raw text data. The preprocessing module performs tokenization, lemmatization, stopword removal, and text normalization to prepare the data for further analysis. It may also involve handling domain-specific vocabulary, such as medical terms, technical jargon, or regional slang, to ensure accurate processing. This step helps remove irrelevant information and structure the data in a standardized format suitable for machine learning models.

Following preprocessing, the system employs feature extraction methods to transform the raw text into a form that can be easily understood by machine learning models. The system utilizes word embeddings (e.g., Word2Vec, GloVe) and contextual embeddings (e.g., BERT, GPT) to represent words or sentences as high-dimensional vectors. These embeddings capture the semantic and syntactic properties of language, allowing the system to understand the meaning of words in context. Additionally, for domain-specific tasks, custom embeddings are generated by training models on specialized corpora, ensuring better accuracy for specific applications.

The core of the system relies on deep learning models designed to handle the complexity of natural language. The invention primarily employs transformer architectures, which use self-attention mechanisms to capture contextual information in text. Unlike traditional RNN-based models, transformers can process entire sequences of words in parallel, making them more efficient for large-scale datasets. These models are fine-tuned using task-specific data to enhance their performance for particular NLP tasks. The use of transfer learning allows for leveraging pre-trained models, reducing the amount of labeled data required for training and improving the model's adaptability to new tasks or languages.

The training process is optimized using techniques such as transfer learning and reinforcement learning. Transfer learning allows the model to apply knowledge learned from one domain or task to another, significantly reducing the amount of domain-specific data needed for training. Reinforcement learning techniques are also employed to further refine the model by rewarding the system for making correct predictions, thus enabling continuous improvement over time.

The scalability of the system is a key feature, allowing it to efficiently handle large datasets. The invention implements distributed computing techniques, enabling the processing of massive amounts of unstructured text data across multiple processors or servers. This ensures that the system remains performant even when processing vast quantities of information in real time.

The system is designed to be adaptable to new domains, languages, and tasks. By utilizing a modular architecture, the system can be extended to include new features or retrained on different datasets without requiring major changes to the overall framework. This flexibility ensures that the system can evolve with emerging needs in NLP, such as adapting to new languages or dialects and responding to advances in machine learning research.

Finally, the system integrates a real-time processing layer that enables low-latency NLP applications. Real-time processing is essential for use cases such as chatbots, virtual assistants, and real-time sentiment analysis, where quick response times are crucial. The system can be optimized to provide high-throughput, low-latency responses while maintaining the accuracy and scalability required for large-scale applications.

In the first embodiment, the invention is applied to perform sentiment analysis on customer feedback for a large e-commerce platform. The raw customer reviews are preprocessed to remove noise such as punctuation, special characters, and irrelevant words. The feature extraction module then generates word embeddings using a combination of pre-trained embeddings such as GloVe for general-purpose words and custom embeddings trained on e-commerce-specific language for better handling of product names, features, and industry-specific terms.

A transformer-based model is employed to classify the sentiment of the feedback as positive, negative, or neutral. The model is fine-tuned using labeled data collected from customer feedback and is further enhanced with reinforcement learning to improve prediction accuracy over time. The system is able to process thousands of customer reviews in real-time, enabling the platform to quickly assess overall customer satisfaction and address issues promptly.

In the second embodiment, the invention is applied to a machine translation system designed for multilingual customer support. The system processes customer inquiries in various languages, translating them into a target language (e.g., English) to be addressed by a support team. The preprocessing module cleans the input text, handling language detection and standardization of terminology across different languages.
A transformer model such as GPT or BERT is employed, fine-tuned on a large corpus of multilingual data, to translate customer inquiries accurately while preserving context and meaning. The system also includes a feature to adapt to specific customer support domains by training the model on historical customer service data, which helps improve translation quality for specific use cases such as technical support or billing inquiries. The system can handle real-time multilingual communication, enabling businesses to provide timely support to customers in different regions.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation , Claims:1.A method for processing natural language data, comprising:
Preprocessing text data to remove noise and structure the data into a standardized format, including tokenization, lemmatization, and stopword removal;
Extracting features from the text data using one or more embedding techniques, including word embeddings, contextual embeddings, or domain-specific embeddings;
Training a machine learning model using the extracted features, wherein the model is a deep learning-based model selected from the group consisting of transformers, recurrent neural networks (RNNs), and convolutional neural networks (CNNs);
Fine-tuning the model using domain-specific data to improve performance on specific tasks;
Applying the trained model to perform one or more NLP tasks selected from the group consisting of text classification, sentiment analysis, named entity recognition, machine translation, and text summarization.

2.The method of claim 1, wherein the preprocessing further comprises the use of domain-specific vocabulary to improve the accuracy of the feature extraction process.

3.The method of claim 1, wherein the machine learning model is a transformer-based architecture, including a self-attention mechanism to enhance contextual understanding of text.

4.The method of claim 1, further comprising the step of applying transfer learning to adapt the model to new domains or languages by leveraging pre-trained models.

Documents