Search Results for "d&d"

...Ghawwas V4.0 provides the following main functions: a. Frequency list for single word and N-Grams b. Concordance c. Collocation (MI, CHI Squared, LL, T-Score, Z Score, Dice, Log Dice, Weirdness Coefficient) d. Lexical patterns search e. Two corpora frequency profile comparison based on MI, CHI, LL, T-Score, Z Score, Dice, Log Dice, Weirdness Coefficient f. Accept Windows and UTF-8 character encoding g. Accept TXT, DOC, DOCX, RTF and HTML formats h. Export the processing results in CSV file format

...The WNLT project delivers four core NLP modules; a) Word Segmentation for separating text into words b) Sentence Boundary Disambiguation for finding sentence boundaries c) Part of Speech Tagger for determining the part of speech of each word d) Morphological Analyser for identifying the root form (lemma) of words. The modules are written in JAVA and ‘wrapped’ for execution under the General Architecture for Text Engineering (GATE) framework. The project also includes CYMRIE an adapted version for Welsh of the GATE - ANNIE Named Entity Recognition (NER) application for a range of entities such as Persons, Organisations, Locations, and date and time expressions. ...

...The WNLT project delivers four core NLP modules; a) Word Segmentation for separating text into words b) Sentence Boundary Disambiguation for finding sentence boundaries c) Part of Speech Tagger for determining the part of speech of each word d) Morphological Analyser for identifying the root form (lemma) of words. The modules are written in JAVA and ‘wrapped’ for execution under the General Architecture for Text Engineering (GATE) framework. The project also includes CYMRIE an adapted version for Welsh of the GATE - ANNIE Named Entity Recognition (NER) application for a range of entities such as Persons, Organisations, Locations, and date and time expressions.

...The jar can be used to (a) get all the terms in the corpus (b) get the document frequency (DF) and inverse document frequency (IDF) of all the terms in the corpus (c) get the TF-IDF of each document in the corpus (d) get each term with their frequency (no. of presence), term frequency (TF) and TF-IDF in every document