データ抽出器

データ抽出器を使用して、さまざまなドキュメントや他のソースから関連する情報を取得できます。

ドキュメントの種類には、主に次の 3 つのカテゴリがあります。

• Structured documents - have a fixed format and are easy to process, guiding you to fill in the required data in precise fields. These documents are designed to comprise a certain type of data. Examples of structured documents: tax forms, surveys, questionnaires, etc.
• Semi-structured documents - have both a fixed format and variable parts. Semi-structured documents do not have a fixed format in the sense that they are not bound to specified data fields like structured documents, but they contain a predictable set of information, for example an invoice always contains a unique identifier, a date, or an invoice number but the placement might vary depending on the provider. These documents mainly contain label:value pairs and may contain paragraphs as well. Example of semi-structured documents: invoices, receipts, purchase orders, utility bills, etc.
• Unstructured documents - the information is not organized according to a fixed format. These documents mainly contain plain text, most of the data is in unstructured form inside the text. Examples of unstructured documents: contracts, emails, health records, etc.

データ抽出器は、ドキュメントからデータを抽出する方法によって異なります。この点において、抽出器には次の 2 つの種類があります。

• Fixed output extractors - trained to extract a predefined set of information from a document; for example the Invoice extractor always tries to extract the company name, address, total sum, etc.
• Question-answering extractors - trained to answer questions based on a given context. These extractors rely on natural language understanding to parse the text and figure out what is the exact value that needs to be extracted from the text and provide an appropriate answer or even choose an option out of a list of given options.

Clipboard AI uses the following set of data extractors:

• ユニバーサル抽出器
• Specific documents extractors
• Plain-text extractor
• Tables and name-value pairs extractor

ユニバーサル抽出器

The Universal extractor is the default option to extract data from your documents. It scans your data (plain text or tabular) and decides the best solution to extract it. It uses a combination of the existing extractors and it also allows queries to find the best match in your data.

Learn how to interact with the Universal extractor.

特定のドキュメント抽出器

The Specific documents extractors are a fixed-output set of extractors trained on specific document types. Each document type is extracted using its corresponding Document Understanding machine learning model, as follows:

• 請求書 (Invoice)
• パスポート
• 領収書
• ID カード
• W-2 フォーム (米国の源泉徴収票)
• 公共料金の請求書
• Purchase order
• Web/デスクトップ フォーム

ドキュメントの種類に基づいて、使用する Document Understanding モデルを選択できます。

プレーン テキスト抽出器

The Plain-text extractor is a question-answering extractor that uses GPT3 to retrieve data from plain text documents, webpages, emails, etc. It can be used either for semi-structured documents to handle the variable parts or for unstructured documents where the layout is irrelevant.

この抽出器では、データの意味の理解がサポートされており、質問応答機能の他に、要約、機械翻訳、ドキュメントの種類の分類、感情検出などの高度な機能もあります。

表および名前と値のペア抽出器

The Tables and name-value pairs extractor is a fixed output extractor which works best for documents containing Tables and Name:Value pairs.