Natural Language Processing API | Developer Portal | Cortical.io
Natural Language Processing API | Developer Portal | Cortical.io
Our APIs build the basis for extracting insights and deriving actionable information from unstructured textual data, ultimately leading to improved decision-making, operational efficiency, and enhanced customer experiences.
Get startedKeyword extraction can be applied to various business use cases to extract important terms or phrases from text data. It helps organizations extract actionable insights from large volumes of text data, enabling them to make informed decisions, improve their strategies, and enhance their overall performance.
Keyword extraction can be used by digital marketing teams to improve search engine optimization (SEO) efforts. Businesses can extract keywords from their website content, blog posts, or product descriptions to identify the most relevant and high-impact keywords and optimize their content accordingly in order to increase their website's visibility in search engine results pages (SERPs) and attract more organic traffic.
Keyword extraction can also be applied to competitive analysis to identify the keywords and phrases used by competitors in their websites, press releases, or social media profiles. This information can help businesses gain insights into their competitors' strategies, discover new market trends, and adapt their own marketing and product development strategies accordingly.
Keyword extraction can assist businesses in analyzing customer reviews, survey responses, and social media comments. By extracting keywords from these sources, companies can identify recurring themes, positive or negative sentiments, and specific issues or features mentioned by customers. This information allows businesses to focus on customer needs in product development, customer service, and marketing campaigns, ultimately leading to higher customer satisfaction and loyalty.
Language detection enables businesses to efficiently handle multilingual aspects of their operations and provide better services to customers.
Many businesses operate globally and provide customer support through various channels, including email, chat, and social media. Language detection can be used to automatically identify the language of incoming customer inquiries so that these inquiries can be routed to the appropriate customer support agents. This improves the efficiency of customer support operations, reduces response times, and enhances the overall customer experience.
In industries such as finance, healthcare, and legal, businesses deal with a multitude of documents and communications that may span multiple languages. Language detection can be used to automatically identify the languages within these documents. This capability is essential for compliance, regulatory reporting, and legal requirements.
For instance, financial institutions may need to ensure that their disclosures are provided in multiple languages to meet international banking standards. Legal firms may need to analyze multilingual contracts and documents for litigation purposes. Language detection simplifies the process of managing and analyzing such documents, reducing the risk of compliance violations and legal disputes.
Comparing text based on semantic similarity is a highly efficient method to determine how similar two pieces of text or concepts are in terms of their meaning.
Semantic similarity can be employed to improve the accuracy of search engines. When a user submits a query, the system calculates the semantic similarity between the query and documents in its database and rank documents not just based on keyword matching but also on how closely the content matches the user's intent.
Semantic similarity can help recommendation systems, like those used by streaming platforms, e-commerce websites, and social media, provide more personalized recommendations by identifying items (e.g., movies, products, or articles) that are semantically similar to what the user has shown interest in.
Last but not least, semantic similarity comparison is crucial in the field of plagiarism detection and content filtering as, for example, it helps educational institutions and content platforms comparing submitted essays, articles, or research papers with a database of existing content.
Text segmentation is the process of dividing a continuous text into smaller, meaningful units or segments.
Text segmentation is often employed in sentiment analysis and opinion mining to break down long paragraphs or documents into smaller segments, such as sentences or phrases and analyze sentiment at a finer level of granularity.
In machine translation systems, where one language is automatically translated into another, text segmentation is crucial for identifying the boundaries of sentences or phrases within a source text.
Named Entity Recognition (NER) is a critical NLP task that involves identifying and categorizing named entities, such as names of people, places, organizations, dates, and more, within a text. Text segmentation plays a key role in NER by segmenting a text into words or tokens and then tagging each token with its corresponding entity label.
Text labeling, also known as text annotation or text tagging, involves categorizing or marking pieces of text to train machine learning models or organize and analyze textual data.
Businesses can use text labeling to classify customer reviews, social media comments, or survey responses into categories like positive, negative, or neutral sentiment. This helps in gauging public opinion, monitoring brand reputation, and identifying areas for improvement in products or services.
Publishers, content platforms, and e-commerce websites can use text labeling to categorize articles, products, or other content. This enables users to easily find relevant information, products, or services. For example, a news aggregator can label news articles by topic (e.g., politics, sports, entertainment), while an e-commerce platform can categorize products by type (e.g., clothing, electronics, home decor).
Email providers and messaging platforms employ text labeling to detect and filter out spam or malicious content. By labeling incoming messages as either spam or not, these systems can protect users from unwanted or harmful messages. Text labeling is crucial for training machine learning models to adapt to evolving spamming techniques.