6 Best NLP Tools: AI Tools for Content Excellence
3 Open Source NLP Tools For Data Extraction - InfoWorld
Unstructured text and data are like gold for business applications and the company bottom line, but where to start? Here are three tools worth a look.
Developers and data scientists use generative AI and large language models (LLMs) to query volumes of documents and unstructured data. Open source LLMs, including Dolly 2.0, EleutherAI Pythia, Meta AI LLaMa, StabilityLM, and others, are all starting points for experimenting with artificial intelligence that accepts natural language prompts and generates summarized responses.
"Text as a source of knowledge and information is fundamental, yet there aren't any end-to-end solutions that tame the complexity in handling text," says Brian Platz, CEO and co-founder of Fluree. "While most organizations have wrangled structured or semi-structured data into a centralized data platform, unstructured data remains forgotten and underleveraged."
If your organization and team aren't experimenting with natural language processing (NLP) capabilities, you're probably lagging behind competitors in your industry. In the 2023 Expert NLP Survey Report, 77% of organizations said they planned to increase spending on NLP, and 54% said their time-to-production was a top return-on-investment (ROI) metric for successful NLP projects.
Use cases for NLPIf you have a corpus of unstructured data and text, some of the most common business needs include
Sometimes, having NLP capabilities bundled into a platform or application is desirable. For example, LLMs support asking questions; AI search engines enable searches and recommendations; and chatbots support interactions. Other times, it's optimal to use NLP tools to extract information and enrich unstructured documents and text.
Let's look at three popular open source NLP tools that developers and data scientists are using to perform discovery on unstructured documents and develop production-ready NLP processing engines.
The Natural Language Toolkit (NLTK), released in 2001, is one of the older and more popular NLP Python libraries. NLTK boasts more than 11.8 thousand stars on GitHub and lists over 100 trained models.
"I think the most important tool for NLP is by far Natural Language Toolkit, which is licensed under Apache 2.0," says Steven Devoe, director of data and analytics at SPR. "In all data science projects, the processing and cleaning of the data to be used by algorithms is a huge proportion of the time and effort, which is particularly true with natural language processing. NLTK accelerates a lot of that work, such as stemming, lemmatization, tagging, removing stop words, and embedding word vectors across multiple written languages to make the text more easily interpreted by the algorithms."
NLTK's benefits stem from its endurance, with many examples for developers new to NLP, such as this beginner's hands-on guide and this more comprehensive overview. Anyone learning NLP techniques may want to try this library first, as it provides simple ways to experiment with basic techniques such as tokenization, stemming, and chunking.
spaCyspaCy is a newer library, with its version 1.0 released in 2016. SpaCy supports over 72 languages and publishes its performance benchmarks, and it has amassed more than 25,000 stars on GitHub.
"spaCy is a free, open-source Python library providing advanced capabilities to conduct natural language processing on large volumes of text at high speed," says Nikolay Manchev, head of data science, EMEA, at Domino Data Lab. "With spaCy, a user can build models and production applications that underpin document analysis, chatbot capabilities, and all other forms of text analysis. Today, the spaCy framework is one of Python's most popular natural language libraries for industry use cases such as extracting keywords, entities, and knowledge from text."
Tutorials for spaCy show similar capabilities to NLTK, including named entity recognition and part-of-speech (POS) tagging. One advantage is that spaCy returns document objects and supports word vectors, which can give developers more flexibility for performing additional post-NLP data processing and text analytics.
Spark NLPIf you already use Apache Spark and have its infrastructure configured, then Spark NLP may be one of the faster paths to begin experimenting with natural language processing. Spark NLP has several installation options, including AWS, Azure Databricks, and Docker.
"Spark NLP is a widely used open-source natural language processing library that enables businesses to extract information and answers from free-text documents with state-of-the-art accuracy," says David Talby, CTO of John Snow Labs. "This enables everything from extracting relevant health information that only exists in clinical notes, to identifying hate speech or fake news on social media, to summarizing legal agreements and financial news.
Spark NLP's differentiators may be its healthcare, finance, and legal domain language models. These commercial products come with pre-trained models to identify drug names and dosages in healthcare, financial entity recognition such as stock tickers, and legal knowledge graphs of company names and officers.
Talby says Spark NLP can help organizations minimize the upfront training in developing models. "The free and open source library comes with more than 11,000 pre-trained models plus the ability to reuse, train, tune, and scale them easily," he says.
Best practices for experimenting with NLPEarlier in my career, I had the opportunity to oversee the development of several SaaS products built using NLP capabilities. My first NLP was an SaaS platform to search newspaper classified advertisements, including searching cars, jobs, and real estate. I then led developing NLPs for extracting information from commercial construction documents, including building specifications and blueprints.
When starting NLP in a new area, I advise the following:
You may find that the NLP tools used to discover and experiment with new document types will aid in defining requirements. Then, expand the review of NLP technologies to include open source and commercial options, as building and supporting production-ready NLP data pipelines can get expensive. With LLMs in the news and gaining interest, underinvesting in NLP capabilities is one way to fall behind competitors. Fortunately, you can start with one of the open source tools introduced here and build your NLP data pipeline to fit your budget and requirements.
Unstructured, Which Offers Tools To Prep Enterprise Data For LLMs ...
Large language models (LLMs) such as OpenAI's GPT-4 are the building blocks for an increasing number of AI applications. But some enterprises have been reluctant to adopt them, owing to their inability to access first-party and proprietary data.
It's not an easy problem to solve, necessarily — considering that sort of data tends to sit behind firewalls and comes in formats that can't be tapped by LLMs. But a relatively new startup, Unstructured.Io, is trying to remove the roadblocks with a platform that extracts and stages enterprise data in a way that LLMs can understand and leverage.
Brian Raymond, Matt Robinson and Crag Wolfe co-founded Unstructured in 2022 after working together at Primer AI, which was focused on building and deploying natural language processing (NLP) solutions for business customers.
"While at Primer, time and again, we encountered a bottleneck ingesting and pre-processing raw customer files containing NLP data (e.G., PDFs, emails, PPTX, XML, etc.) and transforming it into a clean, curated file that's ready for a machine learning model or pipeline," Raymond, who serves as Unstructured's CEO, told TechCrunch in an email interview. "None of the data integration or intelligent document processing companies were helping to solve this problem, so we decided to form a company and tackle it head-on."
Indeed, data processing and prep tends to be a time-consuming step of any AI development workflow. According to one survey, data scientists spend close to 80% of their time preparing and managing data for analysis. As a result, most of the data companies produce — about two-thirds — goes unused, per another poll.
"Organizations generate vast amounts of unstructured data on a daily basis, which when combined with LLMs can supercharge productivity. The problem is that this data is scattered," Raymond continued. "The dirty secret in the NLP community is that data scientists today still must build artisanal, one-off data connectors and pre-processing pipelines completely manually. Unstructured [delivers] a comprehensive solution for connecting, transforming and staging natural language data for LLMs."
Unstructured provides a number of tools to help clean up and transform enterprise data for LLM ingestion, including tools that remove ads and other unwanted objects from web pages, concatenate text, perform optical character recognition on scanned pages and more. The company develops processing pipelines for specific types of PDFs; HTML and Word documents, including for SEC filings; and — of all things — U.S. Army Officer evaluation reports.
To handle documents, Unstructured trained its own "file transformation" NLP model from scratch and assembled a collection of other models to extract text and around 20 discrete elements (e.G., titles, headers and footers) from raw files. Various connectors — about 15 in total — draw in documents from existing data sources, like customer relationship management software.
"Behind the scenes, we're using a variety of different technologies to abstract away complexity," Raymond said. "For example, for old PDFs and images, we're using computer vision models. And for other file types, we're using clever combinations of NLP models, Python scripts and regular expressions."
Downstream, Unstructured integrates with providers like LangChain, a framework for creating LLM apps, and vector databases such as Weaviate and MongoDB's Atlas Vector Search.
Previously, Unstructured's sole product was an open source suite of these data processing tools. Raymond claims that it's been downloaded around 700,000 times and used by over 100 companies. But to cover development costs — and placate its investors, no doubt — the company's launching a commercial API that'll transform data in 25 different file formats, including PowerPoints and JPGs.
"We've been working with government agencies and have several million in revenue in just a very short period. . . . Since our focus is on AI, we're focused on a sector of the market that's not affected by the broader economic slowdown," Raymond said.
Unstructured has unusually close ties to defense agencies, perhaps a product of Raymond's background. Prior to Primer, he was an active member of the U.S. Intelligence community, serving in the Middle East and then in the White House during the Obama administration before a stint at the CIA.
Unstructured was awarded small business contracts by the U.S. Air Force and U.S. Space Force and partnered with U.S. Special Operations Command (SOCOM) to deploy an LLM "in conjunction with mission-relevant data." Moreover, Unstructured's board includes Michael Groen, a former general and director of the Pentagon's Joint Artificial Intelligence Center, and Mike Brown, who previously led the Department of Defense's Defense Innovation Unit.
The defense angle — a reliable early revenue source — might've been the deciding factor in Unstructured's recent financing. Today, the company announced that it raised $25 million across a Series A and previously undisclosed seed funding round. Madrona led the Series A with participation from Bain Capital Ventures, which led the seed, and M12 Ventures, Mango Capital, MongoDB Ventures and Shield Capital, as well as several angel investors.
Kyle Wiggers was TechCrunch's AI Editor until June 2025. His writing has appeared in VentureBeat and Digital Trends, as well as a range of gadget blogs including Android Police, Android Authority, Droid-Life, and XDA-Developers. He lives in Manhattan with his partner, a music therapist.
The Definition Of Unstructured Data - AOL
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What is "Unstructured Data?"Unstructured data refers to information that does not have a predefined data model or organized format, making it more challenging to store, process, and analyze compared to structured data.
Unlike structured data, which fits neatly into rows and columns in a database, unstructured data is usually in its raw form, often comprising text, images, audio, or video. Because of its complex and diverse nature, unstructured data requires specialized tools and techniques to extract meaningful insights.
Unstructured data is abundant in today's digital world, particularly with the growth of social media, multimedia content, and IoT devices.
However, it cannot be easily queried using traditional database systems, and thus, sophisticated artificial intelligence (AI) and machine learning models are frequently used to analyze and interpret this type of data.
Examples of Unstructured Data:Emails: Emails contain free-form text, attachments, and metadata like sender and receiver information, making them a common example of unstructured data.
Social Media Posts: Tweets, Facebook updates, Instagram comments, and other social media interactions often consist of images, videos, and text that lack formal structure.
Audio Files: Recorded speech, music files, and podcasts are examples of unstructured data that need processing to extract useful information, such as transcriptions.
Videos: Videos from YouTube, TikTok or security cameras provide a large amount of data in a non-organized format, requiring tools like computer vision to interpret them.
Customer Reviews: Online reviews of products or services are typically written in natural language, with no fixed format, making them unstructured but valuable for sentiment analysis.
Log Files: System logs from servers, sensors, or applications often contain unstructured text with important information hidden in the noise.
Web Pages: The content of websites, including blog posts, articles, and multimedia, does not follow a strict format and can be considered unstructured.
Natural Language Processing (NLP): AI uses NLP to process and analyze unstructured text data, such as Facebook posts and customer reviews, to extract sentiments and meaning.
Image Recognition: AI models analyze unstructured image data to identify objects, faces, or scenes, which is useful for security systems and content moderation.
Speech-to-Text Conversion: AI tools convert unstructured audio data into structured text, enabling applications like virtual assistants or transcription services.
Sentiment Analysis: AI uses unstructured data from customer feedback, online reviews, or social media to determine public opinion and attitudes towards a brand or product.
Predictive Analytics: AI processes unstructured data like emails, reports, and social media conversations to forecast future trends and behaviors in business or society.
Chatbots: AI-driven chatbots use unstructured conversational data to generate responses and engage with users in a natural and dynamic way.
Content Recommendations: AI models analyze unstructured data from user behavior, such as videos watched or articles read, to suggest personalized content recommendations.
Rich in Information: Unstructured data offers deep insights, as it includes vast and diverse forms of content like text, video, and audio that can capture nuances and context.
Highly Abundant: The majority of data generated today is unstructured, which means businesses have access to massive amounts of information for analysis and decision-making.
Improves AI Accuracy: Training AI models on unstructured data, like images or speech, helps improve the performance and accuracy of AI applications like voice assistants or image recognition.
Unlocks New Insights: Unstructured data can provide insights that structured data cannot, such as detecting customer sentiment, identifying trends, or predicting user preferences.
Greater Flexibility: Unstructured data doesn't require a predefined format, making it easier to capture and store diverse information without the need for rigid schemas.
Supports Advanced Analytics: Unstructured data enables advanced techniques like deep learning and neural networks to tackle problems that structured data alone cannot solve, such as image processing or language understanding.
Enables Personalization: Analyzing unstructured data allows businesses to provide more personalized services or content recommendations based on individual preferences.
Difficult to Process: Unlike structured data, unstructured data requires more advanced tools, techniques, and algorithms to organize and analyze, making it more time-consuming and complex.
Storage Challenges: Due to its variability and size, unstructured data can take up large amounts of storage space and is harder to organize compared to structured data.
Lack of Standardization: Unstructured data doesn't adhere to a predefined format, which makes it harder to integrate into traditional databases and analytical tools.
Requires Advanced AI Models: Extracting value from unstructured data often requires sophisticated AI algorithms, which may require significant computational resources and expertise.
Inconsistencies in Data Quality: Unstructured data can vary in quality, with noise, irrelevant information, or inconsistencies that can affect the accuracy of AI models and analyses.
Harder to Query: Unlike structured data, which can be easily queried in databases, unstructured data requires specialized query systems or AI models to retrieve specific information.
Security and Privacy Concerns: The massive amounts of unstructured data, particularly from social media and IoT devices, raise concerns about privacy and data security, especially when personal information is involved.
Unstructured data is a vast and growing resource that provides immense value but also poses unique challenges in terms of storage, processing, and analysis.
While it offers deeper insights and supports advanced AI applications like natural language processing and image recognition, it also requires more sophisticated tools and models to extract meaningful information.
Despite its limitations, unstructured data is indispensable for modern AI, enabling personalization, sentiment analysis, and other complex tasks that go beyond the capabilities of structured data alone.
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