vocabulary

问题 I try to do typo correction with spaCy, and for that I need to know if a word exists in the vocab or not. If not, the idea is to split the word in two until all segments do exist. As example, "ofthe" does not exist, "of" and "the" do. So I first need to know if a word exists in the vocab. That's where the problems start. I try: for token in nlp("apple"): print(token.lemma_, token.lemma, token.is_oov, "apple" in nlp.vocab) apple 8566208034543834098 True True for token in nlp("andshy"): print

问题 I try to do typo correction with spaCy, and for that I need to know if a word exists in the vocab or not. If not, the idea is to split the word in two until all segments do exist. As example, "ofthe" does not exist, "of" and "the" do. So I first need to know if a word exists in the vocab. That's where the problems start. I try: for token in nlp("apple"): print(token.lemma_, token.lemma, token.is_oov, "apple" in nlp.vocab) apple 8566208034543834098 True True for token in nlp("andshy"): print

来源： https://stackoverflow.com/questions/63982177/how-to-tokenize-new-vocab-in-spacy

问题 What is indexing? What is full text? I know the answers to both questions, but I can't expose those answers in the exact way to an interviewer: indexing means something like index in book fulltext means for search string Can please give me very simple definition for each of these questions? 回答1: An index in mysql is a mapping from each value in a column (or values in a set of columns) to the rows containing that value in that column (or those values in the set of columns). A full text index

问题 I have recently started working with semantic web and linked data technologies, I have been always confused about one thing though. What is the difference between an Ontology and a vocabulary? Which is preferable? 回答1: In the driest sense, a "vocabulary" is a context-less list of terms, with no defined interrelationships. "Ontology" is meatier, implying the presence of interrelationships, axioms, classes, etc. Nevertheless, the term "vocabulary" is almost never used to mean ONLY "list of

问题 There's 2 Dublin Core vocabularies DC terms and DC elements. They define almost the same classes and properties. So what is the key differences between them, and when to use each one. 回答1: Element Set: Namespace: http://purl.org/dc/elements/1.1/ Predefined prefix: dc11 It defines 15 terms . These terms are also published as the standards ISO 15836, ANSI/NISO Z39.85, and RFC 5013. Terms: Namespace: http://purl.org/dc/terms/ Predefined prefixes: dc , dcterms It defines all terms , including the

问题 What's the definition of a Shim? 回答1: From Wikipedia: In computer programming, a shim is a small library that transparently intercepts an API, changing the parameters passed, handling the operation itself, or redirecting the operation elsewhere. Shims typically come about when the behaviour of an API changes, thereby causing compatibility issues for older applications that still rely on the older functionality. In these cases, the older API can still be supported by a thin compatibility layer

问题 I'm trying to use a custom vocabulary in scikit-learn for some clustering tasks and I'm getting very weird results. The program runs ok when not using a custom vocabulary and I'm satisfied with the cluster creation. However, I have already identified a group of words (around 24,000) that I would like to use as a custom vocabulary. The words are stored in a SQL Server table. I have tried so far 2 approaches, but I get the same results at the end. The first one is to create a list, the second

问题 I want to store tuples {a implies c, not c,...} . How do I reference the operators? Is there a specific vocabulary I should use? edited after first comment 来源： https://stackoverflow.com/questions/38776523/referencing-logical-operators

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I want to make a Vocabulary Trainer and I was thinking about the best way to do it. First I searched some translation APIs to use, to avoid having to build my own dictionary, but I found that most of them are paid and some are free but have limitations. So, I think the best way is to make my own dictionary,