Top 10 Best Python Libraries for Natural Language Processing in 2024
![nlp 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)
By carefully evaluating your options and selecting the right library, you can ensure that your NLP project is a success. It is an excellent choice for large-scale NLP projects and is particularly useful for tasks such as named entity recognition and dependency parsing. Libraries that offer a wide range of functionalities can help developers solve complex nlp problems. When it comes to Natural Language Processing (NLP) in Python, there are several libraries available to choose from. In this section, we will compare some of the most popular NLP libraries in terms of ease of use, functionality, community support, and performance. The libraries discussed in this section are some of the best Python libraries for NLP, and they offer a wide range of functionalities for NLP tasks.
GOAT (Good at Arithmetic Tasks): From Language Proficiency to Math Genius – Unite.AI
GOAT (Good at Arithmetic Tasks): From Language Proficiency to Math Genius.
Posted: Wed, 20 Mar 2024 07:00:00 GMT [source]
Gensim is a Python library that specializes in topic modeling and similarity detection. Throughout the training process, LLMs learn to identify patterns in text, which allows a bot to generate engaging responses that simulate human activity. But not every bot is built the same, and your success in using AI is based on your ability to build a bot that meets your users’ specific needs. If the training data lacks diversity, AI could reinforce existing biases in leadership assessments.
Data Privacy And Ethical Use
The future lies in interaction, with AI assistants that can predict and fulfill consumer needs before they even ask. As we head into 2025, the intersection of Account-Based Marketing (ABM) and AI presents unparalleled opportunities for marketers. You can foun additiona information about ai customer service and artificial intelligence and NLP. It is important to note that the selection of a library depends on the specific requirements of the project.
![nlp 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AI tools can provide real-time feedback on behaviors, communication and decision-making. Natural language processing (NLP) can evaluate written and verbal communication, identifying areas for improvement. This instant feedback can allow leaders to adjust and refine their style continuously, enhancing their impact on their teams. In terms of market penetration, Google AI leads due to its vast ecosystem and consumer reach. Google Cloud AI also plays a key role in business AI adoption, offering scalable AI solutions. Google AI’s accessibility and integration into everyday products make it a leader in consumer applications.
How Educational Robotics is Shaping Modern Learning Environments
It is widely considered to be the best Python library for NLP and is an essential tool for beginners looking to get involved in the field of NLP and machine learning. NLTK supports a variety of tasks, including classification, tagging, stemming, parsing, and semantic reasoning. ChatGPT App Overall, understanding NLP is essential for anyone interested in working with natural language data. In a practical sense, there are many use cases for NLP models in the customer service industry. For example, a business can use NLP-based bots to enable seamless agent routing.
OpenAI’s strength lies in the versatility and sophistication of its language models. Its GPT series, particularly GPT-4, stands out for its ability to ChatGPT generate human-like text and handle complex tasks. OpenAI’s dedication to AI ethics and safety ensures that its technology can be used responsibly.
Resistance To AI Integration
As part of an Editorial short series, AZoRobotics takes a look at how the renewable energy sector is harnessing the power of robotic technologies. When it comes to Natural Language Processing, choosing the right Python library can be a daunting task. With so many options available, it’s essential to consider your specific needs and requirements before selecting a library. The performance of an NLP library can have a significant impact on the speed and accuracy of NLP applications. One of the most important factors to consider when choosing an NLP library is its ease of use. Python is a popular language for NLP due to its simplicity, flexibility, and the availability of numerous libraries and frameworks.
In recent years, educational robotics has transformed the way students learn by making STEM subjects—science, technology, engineering, and mathematics—more interactive and accessible. It is an essential library that supports tasks like classification, tagging, stemming, parsing, and semantic reasoning. It also provides a range of datasets and resources that can be used for training and testing NLP models. One of the most popular libraries for NLP is the Natural Language Toolkit (NLTK).
The fusion of AI and ABM is revolutionizing marketing strategies, allowing unprecedented levels of personalization and efficiency. Despite these advancements, The College Investor study raised concerns about Google AI’s reliability in financial matters. For example, the AI provided outdated information on student loans and inaccurate tax advice, which could lead to penalties. The study called for caution when using AI for complex financial decisions, advising users to double-check facts on nuanced topics like investments and taxes. Virtual agents should seamlessly cooperate with existing support systems, namely communication and ticketing tools.
It involves the use of algorithms and statistical models to analyze and extract meaning from natural language data, including text and speech. These technologies help systems process and interpret language, comprehend user intent, and generate relevant responses. Synthetic data generation (SDG) helps enrich customer profiles or data sets, essential for developing accurate AI and machine learning models. Organizations can use SDG to fill gaps in existing data, improving model output scores. OpenAI, however, dominates in cutting-edge AI research, especially in natural language processing. GPT-4 is the gold standard for generative AI, with far-reaching applications in content generation, customer support, and more.
(PDF) Integrating Artificial Intelligence and Natural Language Processing in E-Learning Platforms: A Review of Opportunities and Limitations – ResearchGate
(PDF) Integrating Artificial Intelligence and Natural Language Processing in E-Learning Platforms: A Review of Opportunities and Limitations.
Posted: Wed, 10 Jan 2024 08:00:00 GMT [source]
OpenAI released GPT-4, which improved upon its predecessor in handling complex queries, reasoning, and language generation. OpenAI continues to work on refining its models while expanding partnerships, notably with Microsoft Azure, which has integrated GPT models into its cloud offerings. Google’s ability to integrate AI into its ecosystem gives it a significant edge in market reach. With AI deeply embedded in Search, Maps, and YouTube, Google touches billions of users every day. Google AI’s challenge, though, lies in the accuracy and context-dependence of its information. For instance, financial queries, as highlighted by The College Investor, often result in misleading or outdated advice.
Gensim is a library that is specifically designed for topic modeling and document similarity analysis. The integration of robotics with augmented reality (AR) could offer immersive learning experiences, further enhancing student engagement and understanding. Ultimately, educational robotics will continue to drive interest in STEM, nurturing a generation of innovators and professionals prepared for a technology-driven world. Pattern is a Python library that offers a wide range of functionalities for NLP tasks, including sentiment analysis, part-of-speech tagging, and word inflection.
AI And Leadership Development: Navigating Benefits And Challenges
NLPs break human language down into its basic components and then use algorithms to analyze and pull out the key information that’s necessary to understand a customer’s intent. LLMs are a type of AI model that are trained to understand, generate and manipulate human language. LLMs, such as GPT, use massive amounts of data to learn how to predict and create language, which can then be used to power applications such as chatbots.
AI’s role in leadership development is to enhance personalization, efficiency and growth. Algorithms solve the problem of marketing to everyone by offering hyper-personalized experiences. Netflix’s recommendation engine, for example, refines its suggestions by learning from user interactions. Google AI, on the other hand, continues to push forward with Bard, its conversational AI designed to compete directly with ChatGPT. Bard leverages Google’s vast data and search capabilities to provide users with fast, context-aware responses.
![nlp 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)
AI offers tailored learning experiences by analyzing an individual’s strengths, weaknesses and style. Algorithms can use data from assessments and feedback to design development plans specific to each leader’s growth needs, resulting in more relevant and engaging learning. Python libraries can be used to develop a range of NLP applications, including chatbots, sentiment analysis tools, text summarization tools, and recommendation systems. These applications can be used in a range of industries, from e-commerce to healthcare to finance. Overall, Python has a vibrant NLP community, and these libraries are a testament to the language’s power and flexibility. With the help of these libraries, developers can build sophisticated NLP applications that can understand human language and provide valuable insights.
Python has emerged as the go-to language for NLP due to its simplicity, versatility, and the availability of several powerful libraries. In summary, when choosing an NLP library, developers should consider factors such as ease of use, functionality, community support, and performance. Each library has its own strengths and weaknesses, and the choice ultimately depends on the specific needs of the project.
![nlp 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)
Thus, it’s a great tool for businesses looking to improve through increased customer engagement and fast service delivery. Dialogflow is surely a blessing for people from non-tech backgrounds due to its low coding requirements. Thus, one can use this versatile application to make a career in the rapidly growing artificial intelligence field. NLP is a branch of AI that is used to help bots understand human intentions and meanings based on grammar, keywords and sentence structure.
The study evaluated 100 personal finance searches, showing that while 57% of AI-generated overviews were accurate, 43% had misleading or incorrect information. The AI struggled with nuanced topics like taxes, investments, and student loans. Google’s AI performed well in basic financial definitions but faltered with complex topics requiring context or up-to-date details, such as student loan repayment plans or IRA limits. Let’s examine virtual assistant advancements and their integration with CRM and BI tools. Amid the rapid global expansion of the wind energy sector, the integration of robotics is becoming pivotal for wind farm operators.
AI-based customer journey optimization (CJO) focuses on guiding customers through personalized paths to conversion. This technology uses reinforcement learning to analyze customer data, identifying patterns and predicting the most effective pathways to conversion. OpenAI’s most famous contribution is its Generative Pre-trained Transformers (GPT), which revolutionized the field of Natural Language Processing (NLP). These models, such as GPT-4, excel in language generation, understanding, and creative applications like writing and coding. OpenAI also emphasizes responsible AI use and safety, becoming a leader in discussions about ethical AI deployment.
In addition to these libraries, there are several other options available, including TextBlob and CoreNLP. NLP is a rapidly growing field with numerous applications in various industries, including healthcare, finance, customer service, and marketing. Some of the common tasks in NLP include sentiment analysis, language translation, speech recognition, and text summarization. It is widely considered the best Python library for NLP and is an essential tool for tasks like classification, tagging, stemming, parsing, and semantic reasoning. NLTK is often chosen by beginners looking to get involved in the fields of NLP and machine learning. Another popular library is spaCy, which is recognized as a professional-grade Python library for advanced NLP.
Within the CX industry, LLMs can help a business cut costs and automate processes. LLMs are beneficial for businesses looking to automate processes that require human language. Because of their in-depth training and ability to mimic human behavior, LLM-powered CX systems can do more than simply respond to queries based on preset options. In contrast to less sophisticated systems, LLMs can actively generate highly personalized responses and solutions to a customer’s request. AI may offer insights but lacks the emotional nuance and intuition essential for genuine relationships. Overreliance on AI risks depersonalizing leadership development, reducing it to data points.
![nlp 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)
AI assistants should constantly monitor the information flow from BI and CRM to generate insights on any changes in real-time. Once the first step is completed, data can be used to obtain insights and perform analysis. ML is employed here through algorithms such as classification and regression to find patterns and forecast possible customer behavior. Ultimately, the right Python library for your NLP project will depend on your specific needs and requirements. It’s essential to consider factors such as the size and complexity of your project, your level of experience with NLP, and the specific tasks you need to perform.
- However, one challenge OpenAI faces is scaling its models for broader consumer use.
- OpenAI specializes in large language models, while Google AI is a key player in integrating AI into everyday applications.
- Artificial Intelligence (AI) has become one of the most competitive fields in technology.
- Gensim is another library worth considering, especially if your project involves topic modeling or word embeddings.
- AI relies on data for feedback and insights, raising concerns about privacy, consent and ethical use.
Therefore, it is recommended to explore the features of each library and choose the one that best suits the project’s needs. Developers need to know that they can rely on the community for help and support. The choice of model, parameters, and settings affects the fairness and accuracy of NLP outcomes. Simplified models or certain architectures may not capture nuances, leading to oversimplified and biased predictions.
Alok Kulkarni is Co-Founder and CEO of Cyara, a customer experience (CX) leader trusted by leading brands around the world. The College Investor urged Google to disable AI-generated overviews for financial queries, emphasizing the need for accurate information in areas with financial consequences. Misinformation in financial topics can lead to penalties, poor investment decisions, or even legal issues.
Google AI’s DeepMind division remains at the cutting edge of scientific AI breakthroughs. The division’s protein folding prediction through AlphaFold continues to revolutionize biology and healthcare. Google’s advances in AI-driven drug discovery and healthcare diagnostics show its potential to impact critical industries. OpenAI’s GPT models haven’t made as much progress in scientific applications but continue to dominate the language processing space.
![nlp 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)
Critical areas of concern included student loan repayment plans, IRA contribution limits, and tax advice. The report raised the issue of potential harm to consumers who might follow this misinformation, especially when dealing with taxes, investments, or financial thresholds. A study by The College Investor reveals some shortcomings in Google’s AI-generated summaries, particularly around finance queries.
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