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Data is typically distinguished in spatial data and time-series data, the former can be things like images, maps, graphs, etc. the latter can be e.g. stock-price or a voice recording. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial.
An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document.
BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of their proximity within the document. It is a family of scoring functions with slightly different components and parameters. One of the most prominent instantiations of the function is as follows.
The initial phase uses dense embeddings to retrieve documents. This retrieval can be based on a variety of database formats depending on the use case, such as a vector database, summary index, tree index, or keyword table index. [38] In response to a query, a document retriever selects the most relevant documents.
This work is at the document level. One can also classify a document's polarity on a multi-way scale, which was attempted by Pang [8] and Snyder [9] among others: Pang and Lee [8] expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder [9 ...
In July, reporters found that Google AI gave incorrect, possibly lethal, information about various health queries, like recommending people eat "at least one small rock per day" for vitamins and ...
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