NLP & LANGUAGE

Document Parsing

NLP & Language

Definition

Document parsing is the automatic process of analyzing the structure of a document (invoice, contract, order) and extracting its data in structured format. It combines OCR, NLP, and business rules to identify fields such as amounts, dates, and parties involved. It enables automating the registration of documents that currently require manual data entry.

Related terms

NLP & Language

OCR (Optical Character Recognition)

OCR is the technology that converts images of text (scanned documents, photos of invoices, delivery notes) into editable and searchable digital text. Modern deep learning-based OCR systems handle complex layouts, tables, and handwritten text. It is the first step to digitizing and automating document processes in SMEs.

NLP & Language

Information Extraction

Information extraction is the automatic process of identifying and structuring relevant data from unstructured texts. For example, extracting order numbers, delivery dates, and amounts from supplier emails, or personal data from scanned documents. It drastically reduces data entry time and human errors in SMEs.

Industry-Specific

Electronic Invoicing

Electronic invoicing is the digital process of issuing, sending, and storing invoices in XML format through the Exchange System (SDI). AI automates invoice reconciliation with orders and delivery notes, detects anomalies and errors, and suggests correct accounting entries. It reduces administrative processing time and data entry errors.

EXPLORE

More terms in NLP & Language

NLP (Natural Language Processing)

NLP is the field of AI concerned with the interaction between computers and human language. It includes tasks such as sentiment analysis, entity extraction, machine translation, and text generation. For SMEs, NLP enables automating the analysis of emails, customer reviews, legal documents, and internal communications.

Sentiment Analysis

Sentiment analysis is the NLP technique that automatically determines the emotional tone of a text: positive, negative, or neutral. Companies use it to monitor customer reviews, analyze brand sentiment on social media, and quickly identify satisfaction issues. It can be applied to thousands of texts in seconds.

Named Entity Recognition (NER)

NER is an NLP technique that automatically identifies and classifies entities such as names of people, companies, locations, dates, and monetary amounts within a text. In business, it is used to extract key information from contracts, invoices, emails, and legal documents, saving hours of manual data entry work.

Tokenization

Tokenization is the process of splitting a text into smaller units (tokens) that the AI model can process. Different models use different strategies: some split by words, others by subwords. Understanding tokenization helps estimate API costs (calculated per token) and optimize prompts to stay within the context window limits.

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that understands the context of a word by looking both left and right in the text. It is particularly effective for comprehension tasks such as classification, question answering, and semantic search. Many enterprise search systems use BERT behind the scenes.

Text Classification

Text classification is the task of automatically assigning one or more categories to a document. Practical applications include: routing support tickets by priority, cataloging emails by department, classifying accounting documents by type. Modern models achieve accuracy above 95% on many business classification tasks.

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