# Best Enterprise Natural Language Understanding (NLU) Software ## How Many Natural Language Understanding (NLU) Software Products Does G2 Track? **Total Products under this Category:** 79 ### Category Stats (Aug 2026) - **Average Rating:** 4.42/5 The average rating of products in this category, based on all submitted ratings - **Top Trending Product:** Plasticity (+6.25%) - Among all products in this category, Plasticity recorded the largest rating increase compared to last month _Last updated: August 01, 2026_ ## How Does G2 Rank Natural Language Understanding (NLU) Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 2,500+ Authentic Reviews - 79+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## G2 Grid® for Natural Language Understanding (NLU) Software ![G2 Grid® for Natural Language Understanding (NLU) Software plotting products by satisfaction and market presence](https://www.g2.com/categories/natural-language-understanding-nlu/grids.png?focus%5B%5D=1579500&focus%5B%5D=21473&focus%5B%5D=21472&focus%5B%5D=52116&focus%5B%5D=1375562&focus%5B%5D=334&focus%5B%5D=21645) Highlighted products: Claude, Google Cloud Translation API, Google Cloud Natural Language API, Amazon Comprehend, Azure AI Language, InMoment Experience Improvement (XI) Platform, and NLTK. Underlying data: [Grid® JSON](https://www.g2.com/categories/natural-language-understanding-nlu/grids.json?focus%5B%5D=claude-2025-12-11&focus%5B%5D=google-cloud-translation-api&focus%5B%5D=google-cloud-natural-language-api&focus%5B%5D=amazon-comprehend&focus%5B%5D=azure-ai-language&focus%5B%5D=inmoment-experience-improvement-xi-platform&focus%5B%5D=nltk&segment=enterprise) **Sponsored** ### Google Cloud Translation API Make your content and apps multilingual with fast, dynamic machine translation available in thousands of language pairs. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=2270&secure%5Bchosen_at%5D=2026-08-02T12%3A01%3A52Z&secure%5Bdisplayable_resource_id%5D=2270&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=2270&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=21473&secure%5Bresource_id%5D=2270&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fnatural-language-understanding-nlu%2Fenterprise%3Fopen_modal_url%3D%252Fproducts%252Famazon-comprehend%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fnatural-language-understanding-nlu%25252Fenterprise%2526source%253Dcategory&secure%5Btoken%5D=aa16d13292dc6fc4d55bdd043b3881d05c6d0c9ed04f5048e52fb201629c3092&secure%5Burl%5D=https%3A%2F%2Fcloud.google.com%2Ftranslate%3Futm_source%3DG2%26utm_medium%3Ddisplay%26utm_campaign%3DCloud-SS-DR-GCP-1713658-GCP-DR-NA-US-en-G2-Display-Banner-All-%2525epid%21-%2525ecid%21-translatioapi%26utm_content%3D%257Bdevice%257D-%257Badgroupid%257D-%257Bnetwork%257D-%257Btargetid%257D-%257Bloc_physical_ms%257D-%257Bcampaignid%257D&secure%5Burl_type%5D=custom_url) ### [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) Claude is a state-of-the-art large language model (LLM) developed by Anthropic, designed to serve as a helpful, honest, and harmless AI assistant. With its advanced reasoning capabilities and conversational tone, Claude excels in tasks ranging from complex coding to in-depth financial analysis, making it a versatile tool for developers, enterprises, and financial professionals. Key Features and Functionality: - Advanced Coding Capabilities: Claude Opus 4 leads in coding performance, achieving top scores on benchmarks like SWE-bench and Terminal-bench. It supports sustained, long-running tasks, enabling continuous work for several hours, which is ideal for complex software development projects. - Financial Analysis Tools: Claude integrates seamlessly with financial data platforms such as Databricks and Snowflake, providing a unified interface for market analysis, research, and investment decision-making. It offers direct hyperlinks to source materials for instant verification, enhancing the efficiency of financial workflows. - Extended Context Windows: With an enhanced 500k context window available in Claude Sonnet 4, users can upload extensive documents, including hundreds of sales transcripts or large codebases, facilitating comprehensive analysis and collaboration. - Tool Use and Integration: Claude's extended thinking capabilities allow it to utilize tools like web search during reasoning processes, improving response accuracy. It also supports background tasks via GitHub Actions and integrates natively with development environments like VS Code and JetBrains for seamless pair programming. - Enterprise-Grade Security: The Claude Enterprise plan offers advanced security features, including Single Sign-On (SSO), Just-in-Time Provisioning (JIT), role-based permissions, audit logs, and custom data retention controls, ensuring data safety and compliance for organizations. Primary Value and User Solutions: Claude addresses the need for a reliable and intelligent AI assistant capable of handling complex tasks across various domains. For developers, it enhances productivity through advanced coding support and integration with development tools. Financial professionals benefit from its ability to unify and analyze diverse data sources, streamlining research and decision-making processes. Enterprises gain from its scalable solutions and robust security features, enabling efficient and secure deployment of AI capabilities within their operations. Overall, Claude empowers users to achieve higher efficiency, accuracy, and innovation in their respective fields. **Average Rating:** 4.6/5.0 **Total Reviews:** 408 #### How Do G2 Users Rate Claude? - **Summarization:** 9.9/10 (Category avg: 9.0/10) - **Language Detection:** 9.4/10 (Category avg: 8.8/10) - **Part of Speech Tagging:** 9.0/10 (Category avg: 8.7/10) - **Quality of Support:** 8.1/10 (Category avg: 8.7/10) #### Who Is the Company Behind Claude? - **Seller:** [Anthropic](https://www.g2.com/sellers/anthropic-b3e27488-b6f4-49c9-a8c7-d860a4207ff3) - **HQ Location:** San Francisco, California - **Twitter:** @AnthropicAI 1,440,248 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=f77a1b8402d5e638c3ef9380f68189346d52f732c0a71bc7354dd50d5f3763e8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fanthropicresearch%2F&secure%5Burl_type%5D=linkedin_company_website) 5,178 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Software Engineer, Data Analyst - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 52% Small, 33% Medium #### What Do G2 Reviewers Say About Claude? _AI-generated summary from verified user reviews_ ##### Pros - Users appreciate the **ease of use** of Claude, enabling them to create clear and structured educational content effortlessly. - Users commend Claude for its **ability to maintain long, deep discussions** , enhancing their intellectual and creative projects. - Users find Claude to be **exceptionally helpful** for deep discussions and managing various intellectual tasks effectively. - Users value the **accurate results** provided by Claude, enhancing their efficiency and experience with the tool. - Users value Claude's **clear and structured communication** , enhancing their ability to educate and inform effectively. ##### Cons - Users find **usage limitations** frustrating, especially with access restrictions and inconsistent performance impacting their experience. - Users find Claude has **significant limitations** in visual content support, integration, and responsiveness, affecting efficiency and creativity. - Users find Claude's **limited functionality** restrictive, particularly in visual content support and research speed. - Users find Claude overly cautious and **slow to respond** , which can hinder quick and efficient communication. - Users express concern over **resource limitations** like token usage and search caps, affecting productivity significantly. #### What Are Recent G2 Reviews of Claude? **["Claude Desktop Feels Like a True Coding Partner for Large Codebases"](https://www.g2.com/survey_responses/claude-review-13195714)** **Rating:** 4.5/5.0 stars _— Sree K._ [Read full review](https://www.g2.com/survey_responses/claude-review-13195714) **["Claude’s Impressive Coding and Reasoning—A Real Time-Saver for Engineers"](https://www.g2.com/survey_responses/claude-review-13166368)** **Rating:** 4.0/5.0 stars _— Darshil M._ [Read full review](https://www.g2.com/survey_responses/claude-review-13166368) ### [Google Cloud Translation API](https://www.g2.com/products/google-cloud-translation-api/reviews) Make your content and apps multilingual with fast, dynamic machine translation available in thousands of language pairs. **Average Rating:** 4.4/5.0 **Total Reviews:** 363 #### How Do G2 Users Rate Google Cloud Translation API? - **Summarization:** 8.7/10 (Category avg: 9.0/10) - **Language Detection:** 8.9/10 (Category avg: 8.8/10) - **Part of Speech Tagging:** 8.8/10 (Category avg: 8.7/10) - **Quality of Support:** 8.5/10 (Category avg: 8.7/10) #### Who Is the Company Behind Google Cloud Translation API? - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google 31,899,995 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website) 341,888 employees on LinkedIn® - **Ownership:** NASDAQ:GOOG #### Who Uses This Product? - **Who Uses This:** Software Engineer, Data Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 53% Small, 24% Large #### What Do G2 Reviewers Say About Google Cloud Translation API? _AI-generated summary from verified user reviews_ ##### Pros - Users praise the **impressive quality and accuracy** of translations from Google Cloud Translation API, appreciating its quick integration. - Users find the **ease of use** of Google Cloud Translation API invaluable for quick and efficient translations. - Users appreciate the **remarkable accuracy** of Google Cloud Translation API, enjoying efficient and precise translations across many languages. - Users value the **widespread multilingual support** of Google Cloud Translation API, enhancing global communication effortlessly. - Users value the **high accuracy and automatic language detection** of Google Cloud Translation API for effective global communication. ##### Cons - Users find the **translation accuracy lacking** , especially with local dialects and slang, leading to frustrating experiences. - Users express concerns about the **high pricing** , especially for large volumes and limited free-tier options. - Users express concerns about the **accuracy issues** of Google Cloud Translation API, leading to inconsistent translations across languages. - Users find the **subscription costs can add up quickly** for heavy usage, limiting affordability and flexibility. - Users experience **translation issues** with context and local dialects, affecting reliability and usability of the API. #### What Are Recent G2 Reviews of Google Cloud Translation API? **["Fast, Reliable Multilingual Support with Smooth Backend Integration"](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13184918)** **Rating:** 4.0/5.0 stars _— Muhammed A._ [Read full review](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13184918) **["Fast, Accurate Translations with Seamless Google Cloud Integration"](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13186992)** **Rating:** 4.5/5.0 stars _— Subhashree S._ [Read full review](https://www.g2.com/survey_responses/google-cloud-translation-api-review-13186992) #### What Are G2 Users Discussing About Google Cloud Translation API? - [What advice do you have for developers considering Google Cloud Translation API for multilingual applications?](https://www.g2.com/discussions/what-advice-do-you-have-for-developers-considering-google-cloud-translation-api-for-multilingual-applications) - [What is Google Cloud Translation API used for?](https://www.g2.com/discussions/what-is-google-cloud-translation-api-used-for) - 1 upvote ### [Google Cloud Natural Language API](https://www.g2.com/es/products/google-cloud-natural-language-api/reviews) Derive ideas de texto no estructurado usando el aprendizaje automático de Google. **Average Rating:** 4.3/5.0 **Total Reviews:** 100 #### How Do G2 Users Rate Google Cloud Natural Language API? - **Resumen:** 8.6/10 (Category avg: 9.0/10) - **Detección de idioma:** 8.9/10 (Category avg: 8.8/10) - **Parte del etiquetado de voz:** 8.6/10 (Category avg: 8.7/10) - **Calidad del soporte:** 8.7/10 (Category avg: 8.7/10) #### Who Is the Company Behind Google Cloud Natural Language API? - **Vendedor:** [Google](https://www.g2.com/es/sellers/google) - **Año de fundación:** 1998 - **Ubicación de la sede:** Mountain View, CA - **Twitter:** @google 31,899,995 seguidores en Twitter - **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website) 341,888 empleados en LinkedIn® - **Propiedad:** NASDAQ:GOOG #### Who Uses This Product? - **Who Uses This:** Ingeniero de software - **Top Industries:** Software de Computadora, Tecnología de la información y servicios - **Company Size:** 55% Small, 23% Large #### What Do G2 Reviewers Say About Google Cloud Natural Language API? _AI-generated summary from verified user reviews_ ##### Pros - Los usuarios valoran la **capacidad de carga de la base de datos** de Google Cloud Natural Language API, mejorando la toma de decisiones en los servicios de salud. - Los usuarios valoran el **manejo eficiente de datos** de la API de Google Cloud Natural Language, mejorando la toma de decisiones en los servicios de salud. - A los usuarios les encanta el **soporte de carga de base de datos** de la API de Google Cloud Natural Language, mejorando la toma de decisiones en varias aplicaciones. ##### Cons - Los usuarios encuentran la **interfaz poco amigable** , lo que dificulta entender y utilizar eficazmente la API. #### What Are Recent G2 Reviews of Google Cloud Natural Language API? **["Una elección sólida para el análisis de texto y la detección de sentimientos"](https://www.g2.com/es/survey_responses/google-cloud-natural-language-api-review-13174091)** **Rating:** 4.5/5.0 stars _— Jeni J._ [Read full review](https://www.g2.com/es/survey_responses/google-cloud-natural-language-api-review-13174091) **["Información rápida y precisa de texto con la API de Google Cloud Natural Language"](https://www.g2.com/es/survey_responses/google-cloud-natural-language-api-review-13188869)** **Rating:** 4.5/5.0 stars _— Anjaly T._ [Read full review](https://www.g2.com/es/survey_responses/google-cloud-natural-language-api-review-13188869) ### [Amazon Comprehend](https://www.g2.com/products/amazon-comprehend/reviews) Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; and automatically organizes a collection of text files by topic. **Average Rating:** 4.3/5.0 **Total Reviews:** 83 #### How Do G2 Users Rate Amazon Comprehend? - **Summarization:** 8.6/10 (Category avg: 9.0/10) - **Language Detection:** 8.3/10 (Category avg: 8.8/10) - **Part of Speech Tagging:** 8.8/10 (Category avg: 8.7/10) - **Quality of Support:** 8.5/10 (Category avg: 8.7/10) #### Who Is the Company Behind Amazon Comprehend? - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud 2,232,483 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website) 147,094 employees on LinkedIn® - **Ownership:** NASDAQ: AMZN #### Who Uses This Product? - **Top Industries:** Information Technology and Services, Accounting - **Company Size:** 40% Medium, 39% Small #### What Do G2 Reviewers Say About Amazon Comprehend? _AI-generated summary from verified user reviews_ ##### Pros - Users value the **data protection** features of Amazon Comprehend, ensuring sensitive information is managed securely. - Users value the **valuable insights** that Amazon Comprehend provides, making data analysis from text simpler and more efficient. - Users appreciate the **ease of use** of Amazon Comprehend, enabling valuable insights without needing machine learning experience. - Users appreciate the **valuable insights extraction** capability of Amazon Comprehend, enhancing their data analysis processes efficiently. - Users value the **insightful text analysis** capabilities of Amazon Comprehend, enhancing understanding from various textual sources. ##### Cons - Users find that **accuracy issues** arise without sufficient training data, impacting insights and increasing costs significantly. - Users find the **cost of Amazon Comprehend high** , particularly when dealing with large volumes of data for analysis. - Users find that **insufficient training** can limit accuracy and raise costs for high-volume data processing with Amazon Comprehend. #### What Are Recent G2 Reviews of Amazon Comprehend? **["Powerful, Easy-to-Integrate NLP Insights with Amazon Comprehend"](https://www.g2.com/survey_responses/amazon-comprehend-review-13187739)** **Rating:** 4.0/5.0 stars _— Atharva P._ [Read full review](https://www.g2.com/survey_responses/amazon-comprehend-review-13187739) **["Accurate Pre-Trained NLP Models with Seamless PII Redaction"](https://www.g2.com/survey_responses/amazon-comprehend-review-12955644)** **Rating:** 4.5/5.0 stars _— Ruchi P._ [Read full review](https://www.g2.com/survey_responses/amazon-comprehend-review-12955644) ### [Azure AI Language](https://www.g2.com/es/products/azure-ai-language/reviews) Azure AI Language es un servicio gestionado para desarrollar aplicaciones de procesamiento de lenguaje natural. Identifique términos y frases clave, analice el sentimiento, resuma texto y construya interfaces conversacionales. Use Language para anotar, entrenar, evaluar y desplegar modelos de IA personalizables con mínima experiencia en aprendizaje automático. **Average Rating:** 4.3/5.0 **Total Reviews:** 79 #### How Do G2 Users Rate Azure AI Language? - **Resumen:** 8.3/10 (Category avg: 9.0/10) - **Detección de idioma:** 8.6/10 (Category avg: 8.8/10) - **Parte del etiquetado de voz:** 8.1/10 (Category avg: 8.7/10) - **Calidad del soporte:** 8.4/10 (Category avg: 8.7/10) #### Who Is the Company Behind Azure AI Language? - **Vendedor:** [Microsoft](https://www.g2.com/es/sellers/microsoft) - **Año de fundación:** 1975 - **Ubicación de la sede:** Redmond, Washington - **Twitter:** @microsoft 13,091,739 seguidores en Twitter - **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website) 231,632 empleados en LinkedIn® - **Propiedad:** MSFT #### Who Uses This Product? - **Top Industries:** Software de Computadora, Tecnología de la información y servicios - **Company Size:** 42% Small, 32% Large #### What Are Recent G2 Reviews of Azure AI Language? **["Azure AI Language nos ayuda a entender el sentimiento del cliente y responder de manera más cálida."](https://www.g2.com/es/survey_responses/azure-ai-language-review-12862917)** **Rating:** 4.5/5.0 stars _— Somashekar N._ [Read full review](https://www.g2.com/es/survey_responses/azure-ai-language-review-12862917) **["Potentes y fáciles de implementar conocimientos de PLN con Azure AI"](https://www.g2.com/es/survey_responses/azure-ai-language-review-13017464)** **Rating:** 4.5/5.0 stars _— Rafee N._ [Read full review](https://www.g2.com/es/survey_responses/azure-ai-language-review-13017464) #### What Are G2 Users Discussing About Azure AI Language? - [¿Para qué se utiliza la API de Azure QnA Maker?](https://www.g2.com/es/discussions/what-is-azure-qna-maker-api-used-for) - [¿Qué es API en Microsoft Azure?](https://www.g2.com/es/discussions/what-is-api-in-microsoft-azure) - 1 comment ### [InMoment Experience Improvement (XI) Platform](https://www.g2.com/es/products/inmoment-experience-improvement-xi-platform/reviews) InMoment, el líder en mejorar experiencias y la empresa de plataforma y servicios de CX más recomendada en el mundo, es reconocida por ayudar a los clientes a recopilar e integrar datos de experiencia del cliente para descubrir las ideas que permiten las acciones más inteligentes. Como los marcadores de ritmo en la aplicación de IA galardonada, sus clientes globales activan cada byte de sus datos de experiencia, desde encuestas estructuradas y reseñas sociales hasta conversaciones no estructuradas de registros de llamadas, correos electrónicos, tickets de soporte y transcripciones de chat para descomponer los silos de datos. Esta tecnología única combinada con expertos de la industria internos empodera a las marcas para obtener ROI de sus programas de CX en la mitad del tiempo que sus competidores. Desbloquea el verdadero potencial de cada pieza de datos del cliente con InMoment. Para aprender más, visita inmoment.com **Average Rating:** 4.7/5.0 **Total Reviews:** 314 #### How Do G2 Users Rate InMoment Experience Improvement (XI) Platform? - **Calidad del soporte:** 9.0/10 (Category avg: 8.7/10) #### Who Is the Company Behind InMoment Experience Improvement (XI) Platform? - **Vendedor:** [PG Forsta](https://www.g2.com/es/sellers/pg-forsta) - **Ubicación de la sede:** Salt Lake City, Utah - **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e56037fcec61d213a1f4229eb926938e0c1373395be6ee138691fc922b14a1dc&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fweareinmoment%2F&secure%5Burl_type%5D=linkedin_company_website) 411 empleados en LinkedIn® #### Who Uses This Product? - **Who Uses This:** Gerente de Producto, Gerente de Éxito del Cliente - **Top Industries:** Software de Computadora, Tecnología de la información y servicios - **Company Size:** 47% Small, 39% Medium #### What Are Recent G2 Reviews of InMoment Experience Improvement (XI) Platform? **["Herramientas fáciles de usar. Personal visionario de apoyo."](https://www.g2.com/es/survey_responses/inmoment-experience-improvement-xi-platform-review-9832822)** **Rating:** 5.0/5.0 stars _— Beth W._ [Read full review](https://www.g2.com/es/survey_responses/inmoment-experience-improvement-xi-platform-review-9832822) **["Integración perfecta con POS para encuesta"](https://www.g2.com/es/survey_responses/inmoment-experience-improvement-xi-platform-review-9337685)** **Rating:** 5.0/5.0 stars _— Lakshay D._ [Read full review](https://www.g2.com/es/survey_responses/inmoment-experience-improvement-xi-platform-review-9337685) #### What Are G2 Users Discussing About InMoment Experience Improvement (XI) Platform? - [What is InMoment Experience Intelligence (XI) Platform used for?](https://www.g2.com/es/discussions/what-is-inmoment-experience-intelligence-xi-platform-used-for) ### [NLTK](https://www.g2.com/products/nltk/reviews) NLTK is a platform for building Python programs to work with human language data that provides interfaces to corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. **Average Rating:** 4.4/5.0 **Total Reviews:** 46 #### How Do G2 Users Rate NLTK? - **Summarization:** 7.4/10 (Category avg: 9.0/10) - **Language Detection:** 7.0/10 (Category avg: 8.8/10) - **Part of Speech Tagging:** 7.4/10 (Category avg: 8.7/10) - **Quality of Support:** 8.2/10 (Category avg: 8.7/10) #### Who Is the Company Behind NLTK? - **Seller:** [NLTK Project](https://www.g2.com/sellers/nltk-project) - **HQ Location:** N/A - **Twitter:** @NLTK\_org 2,305 Twitter followers - **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website) 1 employees on LinkedIn® #### Who Uses This Product? - **Who Uses This:** Data Scientist, Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 52% Small, 29% Large #### What Are Recent G2 Reviews of NLTK? **["NLTK - A useful toolkit to start with NLP"](https://www.g2.com/survey_responses/nltk-review-6637986)** **Rating:** 4.0/5.0 stars _— Deepanshu D._ [Read full review](https://www.g2.com/survey_responses/nltk-review-6637986) **["Beast in language processing"](https://www.g2.com/survey_responses/nltk-review-6885408)** **Rating:** 4.5/5.0 stars _— Verified User in Telecommunications_ [Read full review](https://www.g2.com/survey_responses/nltk-review-6885408) #### What Are G2 Users Discussing About NLTK? - [What is NLTK used for?](https://www.g2.com/discussions/what-is-nltk-used-for) Spotlight Categories [Identity and Access Management (IAM) Software](https://www.g2.com/categories/identity-and-access-management-iam) [Business Process Management Software](https://www.g2.com/categories/business-process-management) [Threat Intelligence Software](https://www.g2.com/categories/threat-intelligence) [Lead Intelligence Software](https://www.g2.com/categories/lead-intelligence) [Security Awareness Training Software](https://www.g2.com/categories/security-awareness-training) Similar Categories - [Natural Language Generation (NLG)](/categories/natural-language-generation-nlg) - [Natural Language Processing (NLP) Platforms](/categories/natural-language-processing-nlp-platforms) [Browse Natural Language Understanding (NLU) Themes](/categories/natural-language-understanding-nlu/themes) ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry") BB Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry) Updated Products classified in the overall Natural Language Understanding (NLU) category are similar in many regards and help companies of all sizes solve their business problems. However, enterprise business features, pricing, setup, and installation differ from businesses of other sizes, which is why we match buyers to the right Enterprise Business Natural Language Understanding (NLU) to fit their needs. Compare product ratings based on reviews from enterprise users or connect with one of G2's buying advisors to find the right solutions within the Enterprise Business Natural Language Understanding (NLU) category. In addition to qualifying for inclusion in the Natural Language Understanding (NLU) Software category, to qualify for inclusion in the Enterprise Business Natural Language Understanding (NLU) Software category, a product must have at least 10 reviews left by a reviewer from an enterprise business. Show More * * * ## How Do You Choose the Right Natural Language Understanding (NLU) Software? ### What You Should Know About Natural Language Understanding Software ### What is Natural Language Understanding Software? Natural language understanding, a subset of natural language processing (NLP), makes predictions or decisions based on text data. These learning algorithms can be embedded within applications to provide automated artificial intelligence (AI) features. A connection to a data source is necessary for the algorithm to learn and adapt over time.  Pulling out actionable insights from numerical data housed in ERP systems, CRM software, or accounting software is one thing, but gaining insights from unstructured data sources is invaluable. Without dedicated software for this task, businesses must spend significant time and resources building natural language understanding models or haphazardly investigating the data. These algorithms may be developed with supervised learning or unsupervised learning. Supervised learning involves training an algorithm to determine a pattern of inference by feeding it consistent data to produce a repeated, general output. Human training is necessary for this type of learning. Unsupervised algorithms independently reach an output and are a feature of deep learning algorithms. Reinforcement learning is the final form of machine learning, which consists of algorithms that understand how to react based on their situation or environment. End users of intelligent applications may not be aware that an everyday software tool utilizes a machine learning algorithm to provide automation of some kind. Additionally, machine learning solutions for businesses may come in a machine learning as a service (MLaaS) model. **What Does NLU Stand For?** NLU stands for Natural Language Understanding, which is a subset of natural language processing (NLP). #### What Types of Natural Language Understanding Software Exist? Natural language understanding, at its core, allows machines to understand human language in spoken or written form. There are two key methods this can be accomplished. **Machine learning-based systems** Machine learning algorithms use statistical methods. They learn to perform tasks based on training data they are fed and adjust their methods as more data is processed. Using a combination of machine learning, deep learning, and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning. **Rules-based systems** This system uses carefully designed linguistic rules. This approach was used early in the development of natural language processing and is still used. ### What are the Common Features of Natural Language Understanding Software? The following are some core features within natural language understanding software that can help users better understand text data: **Part-of-speech (POS) tagging:** With POS tagging, users can parse text by parts of speech. This can help break down sentences into component parts to understand them. **Named entity recognition (NER):** Sentences are comprised of various entities, from street names to surnames, places, and more. With NER, one can extract these entities. These extracted entities can then be fed into other systems automatically. **Sentiment analysis:** Language can be positive, negative, or neutral. Using sentiment analysis techniques, one can input text and be given the sentiment (positive or negative) of that text. **Emotion detection:** Similar to sentiment analysis, emotion detection can detect the emotion of human language, whether written or spoken. Despite the research supporting it, this method has come under scrutiny, and its veracity has been challenged. ### What are the Benefits of Natural Language Understanding Software? Natural language understanding is useful in many different contexts and industries. **Application development:** NLU drives the development of AI applications that streamline processes, identify risks, and improve effectiveness. **Efficiency:** NLU-powered applications are constantly improving because of the recognition of their value and the need to stay competitive in the industries in which they are used. They also increase the efficiency of repeatable tasks. A prime example of this can be seen in eDiscovery, where machine learning has created massive leaps in the efficiency with which legal documents are looked through, and relevant ones are identified. **Scalability:** Humans are great at analysis, but their analysis skills can break down when the amount of data is vast and when they need to produce results in record time. NLU-powered technology does not get stressed, pressured, or tired. It can analyze a (relatively) small amount of data or a large text corpus with ease, speed, and accuracy. This can be scaled across a business’ text datasets and various use cases. **Discovering trends:** NLU can do a great job at finding trends and patterns in text data. Through word clouds, graphs and charts, and more, NLU can provide users with deep insight into what is happening beneath the surface. **Empowering non-technical users:** Much NLU technology in the market is no-code or low-code, which allows non-technical users to benefit from the technology. Gone are the days when one needed to go to a data scientist or IT professional to understand language data. ### Who Uses Natural Language Understanding Software? NLU has applications across nearly every industry. Some industries that benefit from NLU applications include financial services, cybersecurity, recruiting, customer service, energy, and regulation. **Marketing:** NLU-powered marketing applications help marketers identify content trends, shape content strategy, and personalize marketing content.  **Finance:** Financial services institutions are increasing their use of NLU-powered applications to stay competitive with others in the industry who are doing the same. Some examples may include trawling through thousands of insurance claims and identifying ones with a high potential to be fraudulent. The process is similar, and the machine learning algorithm can digest the data to achieve the desired outcome quicker. **Human resources:** Resumes are long and filled with words. As such, natural language understanding technology can help recruiters comb through large amounts of resumes and other text data to better understand candidates. ### What are the Alternatives to Natural Language Understanding Software? Alternatives to natural language understanding software can replace this type of software, either partially or completely: [Machine learning software](https://www.g2.com/categories/machine-learning#learn-more) **:** Natural language understanding (NLU) software is specifically connected to and used for text data. If one is looking for more general-use machine learning algorithms, machine learning software would be a good category to pursue. [Text analysis software](https://www.g2.com/categories/text-analysis#learn-more) **:** NLU software is geared toward incorporating NLU capabilities into other applications or systems. Text analysis software, however, is an all-purpose solution built to analyze any text data. Businesses looking to focus on analyzing their text data, such as from surveys, review sites, social media, and customer service tools, can leverage text analysis software to achieve this goal. This software enables businesses to consolidate and analyze their text data within a single platform.  #### Software Related to Natural Language Understanding Software Related solutions that can be used together with natural language understanding software include: [Chatbots software](https://www.g2.com/categories/chatbots) **:** Businesses looking for an off-the-shelf conservational AI solution can leverage chatbots. Tools specifically geared toward chatbot creation helps companies use chatbots off the shelf, with little to no development or coding experience necessary. [Bot platforms software](https://www.g2.com/categories/bot-platforms) **:** Companies looking to build their own chatbot can benefit from bot platforms, which are tools used to build and deploy interactive chatbots. These platforms provide development tools such as frameworks and API toolsets for customizable bot creation. [Intelligent virtual assistants (IVAs)](https://www.g2.com/categories/intelligent-virtual-assistants) **:** Businesses that want conversational AI with strong natural language understanding capabilities should consider IVAs. IVAs understand a range of different intents from a singular utterance and can even understand responses they are not explicitly programmed to using natural language processing (NLP). With the use of machine learning and deep learning, IVAs can grow intelligently and understand a wider vocabulary and colloquial language, as well as provide more precise and correct responses to requests. ### Challenges with Natural Language Understanding Software Software solutions can come with their own set of challenges.  **Data preparation:** A potential concern is preparing the data to be ingested by the NLU tool. The data needs to be stored properly, whether that is in a database or data warehouse. Users may require IT or a dedicated admin to ensure the text analytics tool can consume the data. **Automation pushback:** One of the biggest potential issues with machine learning-powered applications, such as NLU, lies in removing humans from processes. This is particularly problematic when looking at emerging technologies like self-driving cars. By completely removing humans from the product development lifecycle, machines are given the power to decide in life-or-death situations. **Data security:** Companies must consider security options to ensure the correct users see the correct data. They must also have security options that allow administrators to assign verified users different levels of access to the platform. ### Which Companies Should Buy Natural Language Understanding Software? Pattern recognition can help businesses across industries. Effective and efficient predictions can help these businesses make data-informed decisions, such as dynamic pricing based upon a range of data points. **Retail:** An e-commerce site can leverage an NLU application programming interface (API) to create rich, personalized experiences for every user. **Entertainment:** Media organizations can leverage NLU to comb through their scripts and other content to catalog and categorize their material. **Finance:** Financial institutions can analyze contracts and conduct sentiment analysis and named entity recognition to better understand these documents and to scale operations. ### How to Buy Natural Language Understanding Software #### Requirements Gathering (RFI/RFP) for Natural Language Understanding Software If a company is just starting out and looking to purchase their first NLU software, wherever they are in the buying process, g2.com can help select the best machine learning software for them. Taking a holistic overview of the business and identifying pain points can help the team create a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a machine learning platform. #### Compare Natural Language Understanding Software Products **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after the demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is advisable to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition. #### Selection of Natural Language Understanding Software **Choose a selection team** Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities. **Negotiation** Prices on a company's pricing page are not always fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board. ### What Does Natural Language Understanding Software Cost? NLU software is generally available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will usually lack features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, either unlimited or capped at a certain number of hours per billing cycle. Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software. #### Return on Investment (ROI) Businesses decide to deploy machine learning software with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size.  More users will naturally translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform. ### Natural Language Understanding Software Trends **Automation** With the adoption of NLU and the automation of repetitive tasks, businesses can deploy their human workforce to more creative projects. For example, if a machine learning algorithm automatically displays personalized advertisements based on a user’s text, the human marketing team can work on producing creative material. **Voice technology** Voice is a primal method of interacting with others. It is only natural that we now converse with our machines using our voice and that the platforms for said voicebots have seen great success. Voice makes technology feel more human and allows people to trust it more. Voice will prove to be a crucial natural interface that mediates human communication and relationships with devices within an AI-powered world. **Artificial intelligence (AI)** AI is quickly becoming a promising feature of many, if not most, types of software. With machine learning, end users can identify patterns in data, allowing them to make sense of content and help them understand what they are seeing. This pattern recognition is fueling the rise of more powerful, contextually-aware chatbots.