Semantic header detection using pre-trained embeddings

A computer system for detecting one or more semantic headers in one or more tabular structures by utilizing a custom pre-trained embeddings model, comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
• receiving the custom pre-trained embeddings model,
• wherein the received custom pre-trained embeddings model provides a context associated with each term included in each cell from a plurality of cells associated with the one or more tabular structures in one or more documents;
• computing one or more dot product values associated with the one or more tabular structures from the one or more documents based on the context of each cell from the plurality of cells associated with the one or more tabular structures in the one or more documents,
• wherein the one or more tabular structures in the one or more documents is identified by parsing the one or more documents;
• analyzing a plurality of cell contents in each table attribute associated with the one or more tabular structures;
• dividing each cell from the plurality of cells in each tabular structure into two or more buckets,
• wherein a first bucket is populated with a current word vector associated with each cell, and
• wherein a second bucket is populated with one or more remaining word vectors associated with each cell;
• generating one or more similarity feature values based on the computed one or more dot product values,
• wherein the computed one or more dot product values are normalized;
• and
• detecting the one or more semantic headers associated with the one or more tabular structures from the one or more documents based on the one or more similarity feature values.

A computer program product for detecting one or more semantic headers in one or more tabular structures by utilizing a custom pre-trained embeddings model, comprising:

• one or more computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:
• receiving the custom pre-trained embeddings model,
• wherein the received custom pre-trained embeddings model provides a context associated with each term included in each cell from a plurality of cells associated with the one or more tabular structures in one or more documents;
• computing one or more dot product values associated with the one or more tabular structures from the one or more documents based on the context of each cell from the plurality of cells associated with the one or more tabular structures in the one or more documents,
• wherein the one or more tabular structures in the one or more documents is identified by parsing the one or more documents;
• analyzing a plurality of cell contents in each table attribute associated with the one or more tabular structures;
• dividing each cell from the plurality of cells in each tabular structure into two or more buckets,
• wherein a first bucket is populated with a current word vector associated with each cell, and
• wherein a second bucket is populated with one or more remaining word vectors associated with each cell;
• generating one or more similarity feature values based on the computed one or more dot product values,
• wherein the computed one or more dot product values are normalized;
• and
• detecting the one or more semantic headers associated with the one or more tabular structures from the one or more documents based on the one or more similarity feature values.