Сегментация текста в практике письма

Human writing practice

Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both 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 is non trivial, because while some written languages have explicit word boundary markers, such as the word spaces of written English and the distinctive initial, medial and final letter shapes of Arabic, such signals are sometimes ambiguous and not present in all written languages. Compare speech segmentation, the process of dividing speech into linguistically meaningful portions.

Intent segmentation is the problem of dividing written words into keyphrases (2 or more group of words). In English and all other languages the core intent or desire is identified and become the corner stone of the keyphrase Intent segmentation. Core product/service, idea, action & or thought anchor the keyphrase. "[All things are made of atoms]. [Little particles that move] [around in perpetual motion], [attracting each other] [when they are a little distance apart], [but repelling] [upon being squeezed] [into one another]."

Sentence segmentation is the problem of dividing a string of written language into its component sentences. In English and some other languages, using punctuation, particularly the full stop/period character is a reasonable approximation. However even in English this problem is not trivial due to the use of the full stop character for abbreviations, which may or may not also terminate a sentence. For example, Mr. is not its own sentence in "Mr. Smith went to the shops in Jones Street." When processing plain text, tables of abbreviations that contain periods can help prevent incorrect assignment of sentence boundaries. As with word segmentation, not all written languages contain punctuation characters that are useful for approximating sentence boundaries.

Topic analysis consists of two main tasks: topic identification and text segmentation. While the first is a simple classification of a specific text, the latter case implies that a document may contain multiple topics, and the task of computerized text segmentation may be to discover these topics automatically and segment the text accordingly. The topic boundaries may be apparent from section titles and paragraphs. In other cases, one needs to use techniques similar to those used in document classification. Segmenting the text into topics or discourse turns might be useful in some natural processing tasks: it can improve information retrieval or speech recognition significantly (by indexing/recognizing documents more precisely or by giving the specific part of a document corresponding to the query as a result). It is also needed in topic detection and tracking systems and text summarizing problems. Many different approaches have been tried: e. g. HMM, lexical chains, passage similarity using word co occurrence, clustering, topic modeling, etc. It is quite an ambiguous task – people evaluating the text segmentation systems often differ in topic boundaries. Hence, text segment evaluation is also a challenging problem.

Processes may be required to segment text into segments besides mentioned, including morphemes (a task usually called morphological analysis) or paragraphs.