Advanced solution for extraction, analysis and classification of unstructured textual information by Melingo. Extracting insights and knowledge out of free text using an advanced NLP and Machine Learning technology. Get your free demo!
This capability is based on advanced language analysis tools and machine learning technologies that allow to extract the main entities out of free text, such as: names, locations, offensive terms, id’s, weapon, time expressions etc.
A natural-language ability that connects via API to the client’s operational processes (such as call centers and Omni channels platforms) and helps the organization to classify customers’ applications and calls to the right department/persons.
A machine learning solution that helps to extract sentiment automatically out of free text. The solution can handle multiple sentiments in the same paragraph while being able to split properly between positive and negative sentiments and their related domains. The solution also enables to extract customers satisfaction, service quality and communication insights out of discussions with customers.
Morfix Insights can identify the main topics and entities in the survey text and sort them into structured categories automatically. Insights could be: complaints topic and segmentation, abandonment risk, important details on competitors and their offering.
Automated requests tagging can help refer the request to the right place with no need for manual sorting, thus saving time and human resources. This can also be used for automatic replies according to the identified subject.
Classification of transcribed requests into subjects can optimize the calls manageשment: easier search of calls according to their subject, identifying the subjects that appear most often, etc.
Identification of sentiment in social media or any organizational data can help measuring your customers and audience sentiment on a certain topic, product, idea, etc.
Classification of email or other ways of contact requests can help create fast and accurate automatic replies or refer the request automatically to the right department.
Free text fields in medical documents can contain valuable information about the diagnosis or recommendations for follow-up treatment. Conversion of these texts into structured data can make the treatment process more efficient and accurate and save precious time.
Entity extraction can be used to identify keywords such as skills, professions, companies, degrees and more from a candidate's resumé and from the job opening and match them to determine how well the candidate fits for the job.
Free language bots can be a useful tool for easy and fast communication, and they require understanding the intent of the text (what the person meant). Morfix Insights can help identify the intent in the free text, while correcting typos, understanding slang phrases and splitting the sentence to more than one intent, if needed.
Extraction of entities from the texts according to the customer's needs for further classification of drill down. Morfix Insights contains dozens of built-in entity types, such as names, dates, locations, phone numbers, medical terms and many more, and also can extract user-defined entities that are specific for the project.