lemmatization

I'm using the Wordnet Lemmatizer via NLTK on the Brown Corpus (to determine if the nouns in it are used more in their singular form or their plural form). i.e. from nltk.stem.wordnet import WordNetLemmatizer l = WordnetLemmatizer() I've noticed that even the simplest queries such as the one below takes quite a long time (at least a second or two). l("cats") Presumably this is because a web connection must be made to Wordnet for each query?.. I'm wondering if there is a way to still use the Wordnet Lemmatizer but have it perform much faster? For instance, would it help at all for me to download

I have some text in French that I need to process in some ways. For that, I need to: First, tokenize the text into words Then lemmatize those words to avoid processing the same root more than once As far as I can see, the wordnet lemmatizer in the NLTK only works with English. I want something that can return "vouloir" when I give it "voudrais" and so on. I also cannot tokenize properly because of the apostrophes. Any pointers would be greatly appreciated. :) Here 's an old but relevant comment by an nltk dev. Looks like most advanced stemmers in nltk are all English specific: The nltk.stem

This question is a possible duplicate of Lemmatizer in R or python (am, are, is -> be?) , but I'm adding it again since the previous one was closed saying it was too broad and the only answer it has is not efficient (as it accesses an external website for this, which is too slow as I have very large corpus to find the lemmas for). So a part of this question will be similar to the above mentioned question. According to Wikipedia, lemmatization is defined as: Lemmatisation (or lemmatization) in linguistics, is the process of grouping together the different inflected forms of a word so they can

This question already has an answer here: wordnet lemmatization and pos tagging in python 7 answers I build a Plaintext-Corpus and the next step is to lemmatize all my texts. I'm using the WordNetLemmatizer and need the pos_tag for each token in order to do not get the Problem that e.g. loving -> lemma = loving and love -> lemma = love... The default WordNetLemmatizer-POS-Tag is n (=Noun) i think, but how can i use the pos_tag? I think the expected WordNetLemmatizer-POS-Tag are diffrent to the pos_tag i get. Is there a function or something that can help me?!?! in this line i think the word

Dear Community Members, During the pre-processing of data, after splitting the raw_data into tokens, I have used the popular WordNet Lemmatizer to generate the stems. I am performing experiments on a dataset that has 18953 tokens. My question is, does the lemmatization process reduce the size of corpus? I am confused, kindly help in this regard. Any help is appreciated! Lemmatization converts each token (aka form ) in the sentence into its lemma form (aka type ): >>> from nltk import word_tokenize >>> from pywsd.utils import lemmatize_sentence >>> text = ['This is a corpus with multiple

I am working on a project and I need to get the root of a given word (stemming). As you know, the stemming algorithms that don't use a dictionary are not accurate. Also I tried the WordNet but it is not good for my project. I found phpmorphy project but it doesn't include API in Java. At this time I am looking for a database or a text file of english words with their different forms. for example: run running ran ... include including included ... ... Thank you for your help or advise. You could download LanguageTool (Disclaimer: I'm the maintainer), which comes with a binary file english.dict

I'm not a [computational] linguistic, so please excuse my supper dummy-ness in this topic. According to Wikipedia, lemmatisation is defined as: Lemmatisation (or lemmatization) in linguistics, is the process of grouping together the different inflected forms of a word so they can be analysed as a single item. Now my question is, is the lemmatised version of any member of the set {am, is, are} supposed to be "be"? If not, why not? Second question: How do I get that in R or python? I've tried methods like this link, but non of them gives "be" given "are". I guess at least for the purpose of

I'm preparing some table names for an ORM, and I want to turn plural table names into single entity names. My only problem is finding an algorithm that does it reliably. Here's what I'm doing right now: If a word ends with -ies , I replace the ending with -y If a word ends with -es , I remove this ending. This doesn't always work however - for example, it replaces Types with Typ Otherwise, I just remove the trailing -s Does anyone know of a better algorithm? Those are all general rules (and good ones) but English is not a language for the faint of heart :-). My own preference would be to have