Extracting features with the bi-directional LSTM-CNNs-CRF deep learning model common words) can be given less weight when using deep learning to extract features from the corpus. Terms are sorted by TFIDF values, and those with low values (i.e. Ultimately, TFIDF values are a measure of how relevant a word is to a document within a collection of documents. The product of TF and IDF provides a measure of how frequent a term appears in a document multiplied by how unique the word is in the corpus. Term frequency (TF) represents the raw count of a term in a document divided by the total number of terms in the document, while inverse document frequency (IDF) is the number of documents in a corpus divided by the number of documents in which a term appears. This model uses a combination of two metrics, term frequency and inverse document frequency, to give each word within a document a TFIDF value. To begin making sense of unstructured textual data, the term frequency–inverse document frequency (TFIDF) model was applied to the corpus the WPS writing assistant pulls from. Using the TFIDF model to maximize feature extraction A detailed explanation of methods for pre-processing and cleaning text data can be found here.
Nonetheless, this is an important step that preempts processing the data, and can include removing tags, removing accented characters, expanding contractions, removing special characters, removing stopwords, and more. There are a variety of ways to clean textual data, none of which this article will cover in depth. In order to find meaning, machine readable features must be extracted from an unstructured corpus of documents. Unlike structured datasets with a fixed number of dimensions, bodies of text inherently lack structure because the syntax that governs them is so malleable. While both will adhere to the rules, principles, and processes that govern sentence structure, they will make different word choices, create sentences of varying length, and use their own article structures to tell similar (or perhaps dissimilar) stories. To understand the complexity of this problem consider how two journalists from different news outlets might report on the same topic. Tens of millions of dense text documents from which meaningful features must be extracted, to be a bit more precise. Much like any modern problem worth solving, building the WPS writing assistant begins with messy data. Making sense of unstructured textual data
Wps word writer password#
Encryption is supported, which means when you save a document you can choose a custom encryption type and a unique password for opening the document, and another for modifying it.Common formatting is allowed, such as organizing data in columns, changing the orientation of the page, aligning text and objects, adding a header and footer, overlaying a watermark, and using heading styles.
Wps word writer software#
The installer includes other software you can optionally add to your computer, like WPS Spreadsheets and WPS Presentation.Works with Windows, Linux, Mac, Android, and iOS, and can also be used directly from a browser.