Processing raw text intelligently is difficult: most words are rare, and it’s common for words that us based binary options companies that are hiring completely different to mean almost the same thing. The same words in a different order can mean something completely different. Even splitting text into useful word-like units can be difficult in many languages. While it’s possible to solve some problems starting from only the raw characters, it’s usually better to use linguistic knowledge to add useful information.
English is most likely a noun. Linguistic annotations are available as Token attributes . Apple is looking at buying U. Lemma: The base form of the word. Is the token an alpha character? Is the token part of a stop list, i. Tip: Understanding tags and labels Most of the tags and labels look pretty abstract, and they vary between languages.
Rule-based morphology Inflectional morphology is the process by which a root form of a word is modified by adding prefixes or suffixes that specify its grammatical function but do not changes its part-of-speech. I don’t watch the news, I read the paper. The tokenizer consults a mapping table TOKENIZER_EXCEPTIONS, which allows sequences of characters to be mapped to multiple tokens. Each token may be assigned a part of speech and one or more morphological features. The part-of-speech tagger then assigns each token an extended POS tag.
In the API, these tags are known as Token. For words whose POS is not set by a prior process, a mapping table TAG_MAP maps the tags to a part-of-speech and a set of morphological features. Finally, a rule-based deterministic lemmatizer maps the surface form, to a lemma in light of the previously assigned extended part-of-speech and morphological information, without consulting the context of the token. Cy features a fast and accurate syntactic dependency parser, and has a rich API for navigating the tree. The parser also powers the sentence boundary detection, and lets you iterate over base noun phrases, or “chunks”. Text: The original noun chunk text. Root text: The original text of the word connecting the noun chunk to the rest of the parse.
Root dep: Dependency relation connecting the root to its head. Root head text: The text of the root token’s head. The term dep is used for the arc label, which describes the type of syntactic relation that connects the child to the head. As with other attributes, the value of . Dep: The syntactic relation connecting child to head. Head text: The original text of the token head.