Written text segmentation is the process of dividing written text into words or other similar meaningful units. The term applies to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing.
The problem may appear relatively trivial for written languages that have explicit word boundary markers, such as the word spaces of written English or the distinctive initial, medial and final letter shapes of Arabic. When such clues are not consistently available, the task often requires fairly non-trivial techniques, such as statistical decision-making, large dictionaries, as well as consideration of syntactic and semantic constraints.
Natural Language Processing (NLP) text segmentation techniques involves determining the boundaries between words and sentences. This process is not as simple as finding periods (a period may appear for example in a dollar amount), semicolons (may appear for example in an XML entity tag), etc.
When processing plain text, tables of abbreviations that contain periods (Mr. for example) can help prevent incorrect assignment of sentence boundaries. Some text segmentation systems take advantage of any markup like HTML and know document formats like PDF to provide additional evidence for sentence and paragraph boundaries.
See also
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