I'm searching for a Lemmatizer/PoS-tagger for the Italian language, that works on Python. I tried with Spacy, it works but it's not very precise, expecially for verbs it often returns the wrong lemma. NLKT has only english as language. I'm searching for an optimized tool for the Italian language, does it exists?
If it doesn't exist, is it possible, given a corpus, to create it? Whats the work needed to create it?
I also found myself into this problem. I found that one of the best italian lemmatizers is TreeTagger. I preferred it to Spacy's lemmatizer for some projects (I also think that it could be better at POS-tagging). You can also test it online to find out if it is ok for your use case.
I found very useful to use it inside my Spacy pipeline, just for lemmatization, to keep the infrastructure that Spacy provides. This is how you can replace Spacy's lemmatizer with TreeTagger in Python thanks to treetaggerwrapper (you could easily do the same with the POS-tagger):
from treetaggerwrapper import TreeTagger
...
nlp = spacy.load("it_core_news_lg")
TREETAGGER = TreeTagger(TAGDIR="path_to_treetagger", TAGLANG="it")
#Language.component("treetagger")
def treetagger(doc):
tokens = [token.text for token in doc if not token.is_space]
tags = TREETAGGER.tag_text(tokens, tagonly=True)
lemmas = [tag.split("\t")[2].split("|")[0] for tag in tags]
j = 0
for token in doc:
if not token.is_space:
token.lemma_ = lemmas[j]
j += 1
else:
token.lemma_ = " "
return doc
nlp.replace_pipe("lemmatizer", "treetagger")
This could be a useful temporaneous solution.
Me and my friends working on a hobby project and trying to extract data from plain text. Not something too complicated, just trying to extract name, birth date or somethings like that.
Let's say that we have a text file like this,
"Hello my name is John and I'm 22 years old. I'm living in USA and I like playing video games"
We want to fill a table like this
Name: John
Age: 22
From: USA
Looking for NLP since like last week and I don't even know where to start. Every kind of help appreciated.
It looks like NER (Named Entity Recognition) is what you are looking for.
Here a link that explains what NER is.
For the operative part, I suggest you have a look at this, but you can find a lot of free guides on the Internet.
Basically, you will have a code that looks like this, more or less:
import spacy # spaCy is a python module to work with NLP
nlp = spacy.load('en_core_web_sm') # loads english NLP model (small)
sentence = "Apple is looking at buying U.K. startup for $1 billion" # here you will type your sentence
doc = nlp(sentence) # process the sentence with the nlp model and retrieve entities
for ent in doc.ents: # for every entity, print text, start index, end index, label (what type of entity it is)
print(ent.text, ent.start_char, ent.end_char, ent.label_)
I am using a spacy model for german language for extracting named entities such as location names, person names and company names but not getting the proper result as an output. Is there any missing concept which I am unable to figure out precisely.
def city_finder(text_data):
nlp = spacy.load('en_core_web_sm')
doc = nlp(text_data)
for ents in doc.ents:
if(ents.label_ == 'GPE'):
return (ents.text)
This is the code which I had used in order to find the city names from the text data but its accuracy is not very high. When I run this code the result is coming out to be something else instead of the city name. Is there something which I am missing out as part of the Natural Language Processing or any other area?
You have to load a German language model, currently you loaded an English language model (see the prefix "en"). There are two German models available:
de_core_news_sm
de_core_news_md
Source: https://spacy.io/models/de
You can install them via the following command:
python -m spacy download de_core_news_sm
However, there are currently only four different entities supported (and my experience is that without retraining, the entity extraction doesn't work very well in German)
Supported NER: LOC, MISC, ORG, PER
You would have to adapt your code the following way:
def city_finder(text_data):
nlp = spacy.load('de_core_news_sm') # load German language model
doc = nlp(text_data)
for ents in doc.ents:
if(ents.label_ == 'LOC'): # GPE is not supported
return (ents.text)
There are standard libraries available for extracting language specific POS. You can check other libraries for extracting nouns for example Pattern library from CLiPS (refer https://github.com/clips/pattern) implements POS for languages like German and Spanish.
I've tried PorterStemmer and Snowball but both don't work on all words, missing some very common ones.
My test words are: "cats running ran cactus cactuses cacti community communities", and both get less than half right.
See also:
Stemming algorithm that produces real words
Stemming - code examples or open source projects?
If you know Python, The Natural Language Toolkit (NLTK) has a very powerful lemmatizer that makes use of WordNet.
Note that if you are using this lemmatizer for the first time, you must download the corpus prior to using it. This can be done by:
>>> import nltk
>>> nltk.download('wordnet')
You only have to do this once. Assuming that you have now downloaded the corpus, it works like this:
>>> from nltk.stem.wordnet import WordNetLemmatizer
>>> lmtzr = WordNetLemmatizer()
>>> lmtzr.lemmatize('cars')
'car'
>>> lmtzr.lemmatize('feet')
'foot'
>>> lmtzr.lemmatize('people')
'people'
>>> lmtzr.lemmatize('fantasized','v')
'fantasize'
There are other lemmatizers in the nltk.stem module, but I haven't tried them myself.
I use stanford nlp to perform lemmatization. I have been stuck up with a similar problem in the last few days. All thanks to stackoverflow to help me solve the issue .
import java.util.*;
import edu.stanford.nlp.pipeline.*;
import edu.stanford.nlp.ling.*;
import edu.stanford.nlp.ling.CoreAnnotations.*;
public class example
{
public static void main(String[] args)
{
Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma");
pipeline = new StanfordCoreNLP(props, false);
String text = /* the string you want */;
Annotation document = pipeline.process(text);
for(CoreMap sentence: document.get(SentencesAnnotation.class))
{
for(CoreLabel token: sentence.get(TokensAnnotation.class))
{
String word = token.get(TextAnnotation.class);
String lemma = token.get(LemmaAnnotation.class);
System.out.println("lemmatized version :" + lemma);
}
}
}
}
It also might be a good idea to use stopwords to minimize output lemmas if it's used later in classificator. Please take a look at coreNlp extension written by John Conwell.
I tried your list of terms on this snowball demo site and the results look okay....
cats -> cat
running -> run
ran -> ran
cactus -> cactus
cactuses -> cactus
community -> communiti
communities -> communiti
A stemmer is supposed to turn inflected forms of words down to some common root. It's not really a stemmer's job to make that root a 'proper' dictionary word. For that you need to look at morphological/orthographic analysers.
I think this question is about more or less the same thing, and Kaarel's answer to that question is where I took the second link from.
The stemmer vs lemmatizer debates goes on. It's a matter of preferring precision over efficiency. You should lemmatize to achieve linguistically meaningful units and stem to use minimal computing juice and still index a word and its variations under the same key.
See Stemmers vs Lemmatizers
Here's an example with python NLTK:
>>> sent = "cats running ran cactus cactuses cacti community communities"
>>> from nltk.stem import PorterStemmer, WordNetLemmatizer
>>>
>>> port = PorterStemmer()
>>> " ".join([port.stem(i) for i in sent.split()])
'cat run ran cactu cactus cacti commun commun'
>>>
>>> wnl = WordNetLemmatizer()
>>> " ".join([wnl.lemmatize(i) for i in sent.split()])
'cat running ran cactus cactus cactus community community'
Martin Porter's official page contains a Porter Stemmer in PHP as well as other languages.
If you're really serious about good stemming though you're going to need to start with something like the Porter Algorithm, refine it by adding rules to fix incorrect cases common to your dataset, and then finally add a lot of exceptions to the rules. This can be easily implemented with key/value pairs (dbm/hash/dictionaries) where the key is the word to look up and the value is the stemmed word to replace the original. A commercial search engine I worked on once ended up with 800 some exceptions to a modified Porter algorithm.
Based on various answers on Stack Overflow and blogs I've come across, this is the method I'm using, and it seems to return real words quite well. The idea is to split the incoming text into an array of words (use whichever method you'd like), and then find the parts of speech (POS) for those words and use that to help stem and lemmatize the words.
You're sample above doesn't work too well, because the POS can't be determined. However, if we use a real sentence, things work much better.
import nltk
from nltk.corpus import wordnet
lmtzr = nltk.WordNetLemmatizer().lemmatize
def get_wordnet_pos(treebank_tag):
if treebank_tag.startswith('J'):
return wordnet.ADJ
elif treebank_tag.startswith('V'):
return wordnet.VERB
elif treebank_tag.startswith('N'):
return wordnet.NOUN
elif treebank_tag.startswith('R'):
return wordnet.ADV
else:
return wordnet.NOUN
def normalize_text(text):
word_pos = nltk.pos_tag(nltk.word_tokenize(text))
lemm_words = [lmtzr(sw[0], get_wordnet_pos(sw[1])) for sw in word_pos]
return [x.lower() for x in lemm_words]
print(normalize_text('cats running ran cactus cactuses cacti community communities'))
# ['cat', 'run', 'ran', 'cactus', 'cactuses', 'cacti', 'community', 'community']
print(normalize_text('The cactus ran to the community to see the cats running around cacti between communities.'))
# ['the', 'cactus', 'run', 'to', 'the', 'community', 'to', 'see', 'the', 'cat', 'run', 'around', 'cactus', 'between', 'community', '.']
http://wordnet.princeton.edu/man/morph.3WN
For a lot of my projects, I prefer the lexicon-based WordNet lemmatizer over the more aggressive porter stemming.
http://wordnet.princeton.edu/links#PHP has a link to a PHP interface to the WN APIs.
Look into WordNet, a large lexical database for the English language:
http://wordnet.princeton.edu/
There are APIs for accessing it in several languages.
This looks interesting:
MIT Java WordnetStemmer:
http://projects.csail.mit.edu/jwi/api/edu/mit/jwi/morph/WordnetStemmer.html
Take a look at LemmaGen - open source library written in C# 3.0.
Results for your test words (http://lemmatise.ijs.si/Services)
cats -> cat
running
ran -> run
cactus
cactuses -> cactus
cacti -> cactus
community
communities -> community
The top python packages (in no specific order) for lemmatization are: spacy, nltk, gensim, pattern, CoreNLP and TextBlob. I prefer spaCy and gensim's implementation (based on pattern) because they identify the POS tag of the word and assigns the appropriate lemma automatically. The gives more relevant lemmas, keeping the meaning intact.
If you plan to use nltk or TextBlob, you need to take care of finding the right POS tag manually and the find the right lemma.
Lemmatization Example with spaCy:
# Run below statements in terminal once.
pip install spacy
spacy download en
import spacy
# Initialize spacy 'en' model
nlp = spacy.load('en', disable=['parser', 'ner'])
sentence = "The striped bats are hanging on their feet for best"
# Parse
doc = nlp(sentence)
# Extract the lemma
" ".join([token.lemma_ for token in doc])
#> 'the strip bat be hang on -PRON- foot for good'
Lemmatization Example With Gensim:
from gensim.utils import lemmatize
sentence = "The striped bats were hanging on their feet and ate best fishes"
lemmatized_out = [wd.decode('utf-8').split('/')[0] for wd in lemmatize(sentence)]
#> ['striped', 'bat', 'be', 'hang', 'foot', 'eat', 'best', 'fish']
The above examples were borrowed from in this lemmatization page.
If I may quote my answer to the question StompChicken mentioned:
The core issue here is that stemming algorithms operate on a phonetic basis with no actual understanding of the language they're working with.
As they have no understanding of the language and do not run from a dictionary of terms, they have no way of recognizing and responding appropriately to irregular cases, such as "run"/"ran".
If you need to handle irregular cases, you'll need to either choose a different approach or augment your stemming with your own custom dictionary of corrections to run after the stemmer has done its thing.
The most current version of the stemmer in NLTK is Snowball.
You can find examples on how to use it here:
http://nltk.googlecode.com/svn/trunk/doc/api/nltk.stem.snowball2-pysrc.html#demo
You could use the Morpha stemmer. UW has uploaded morpha stemmer to Maven central if you plan to use it from a Java application. There's a wrapper that makes it much easier to use. You just need to add it as a dependency and use the edu.washington.cs.knowitall.morpha.MorphaStemmer class. Instances are threadsafe (the original JFlex had class fields for local variables unnecessarily). Instantiate a class and run morpha and the word you want to stem.
new MorphaStemmer().morpha("climbed") // goes to "climb"
Do a search for Lucene, im not sure if theres a PHP port but I do know Lucene is available for many platforms. Lucene is an OSS (from Apache) indexing and search library. Naturally it and community extras might have something interesting to look at. At the very least you can learn how it's done in one language so you can translate the "idea" into PHP.
.Net lucene has an inbuilt porter stemmer. You can try that. But note that porter stemming does not consider word context when deriving the lemma. (Go through the algorithm and its implementation and you will see how it works)
Martin Porter wrote Snowball (a language for stemming algorithms) and rewrote the "English Stemmer" in Snowball. There are is an English Stemmer for C and Java.
He explicitly states that the Porter Stemmer has been reimplemented only for historical reasons, so testing stemming correctness against the Porter Stemmer will get you results that you (should) already know.
From http://tartarus.org/~martin/PorterStemmer/index.html (emphasis mine)
The Porter stemmer should be regarded as ‘frozen’, that is, strictly defined, and not amenable to further modification. As a stemmer, it is slightly inferior to the Snowball English or Porter2 stemmer, which derives from it, and which is subjected to occasional improvements. For practical work, therefore, the new Snowball stemmer is recommended. The Porter stemmer is appropriate to IR research work involving stemming where the experiments need to be exactly repeatable.
Dr. Porter suggests to use the English or Porter2 stemmers instead of the Porter stemmer. The English stemmer is what's actually used in the demo site as #StompChicken has answered earlier.
In Java, i use tartargus-snowball to stemming words
Maven:
<dependency>
<groupId>org.apache.lucene</groupId>
<artifactId>lucene-snowball</artifactId>
<version>3.0.3</version>
<scope>test</scope>
</dependency>
Sample code:
SnowballProgram stemmer = new EnglishStemmer();
String[] words = new String[]{
"testing",
"skincare",
"eyecare",
"eye",
"worked",
"read"
};
for (String word : words) {
stemmer.setCurrent(word);
stemmer.stem();
//debug
logger.info("Origin: " + word + " > " + stemmer.getCurrent());// result: test, skincar, eyecar, eye, work, read
}
Try this one here: http://www.twinword.com/lemmatizer.php
I entered your query in the demo "cats running ran cactus cactuses cacti community communities" and got ["cat", "running", "run", "cactus", "cactus", "cactus", "community", "community"] with the optional flag ALL_TOKENS.
Sample Code
This is an API so you can connect to it from any environment. Here is what the PHP REST call may look like.
// These code snippets use an open-source library. http://unirest.io/php
$response = Unirest\Request::post([ENDPOINT],
array(
"X-Mashape-Key" => [API KEY],
"Content-Type" => "application/x-www-form-urlencoded",
"Accept" => "application/json"
),
array(
"text" => "cats running ran cactus cactuses cacti community communities"
)
);
I highly recommend using Spacy (base text parsing & tagging) and Textacy (higher level text processing built on top of Spacy).
Lemmatized words are available by default in Spacy as a token's .lemma_ attribute and text can be lemmatized while doing a lot of other text preprocessing with textacy. For example while creating a bag of terms or words or generally just before performing some processing that requires it.
I'd encourage you to check out both before writing any code, as this may save you a lot of time!
df_plots = pd.read_excel("Plot Summary.xlsx", index_col = 0)
df_plots
# Printing first sentence of first row and last sentence of last row
nltk.sent_tokenize(df_plots.loc[1].Plot)[0] + nltk.sent_tokenize(df_plots.loc[len(df)].Plot)[-1]
# Calculating length of all plots by words
df_plots["Length"] = df_plots.Plot.apply(lambda x :
len(nltk.word_tokenize(x)))
print("Longest plot is for season"),
print(df_plots.Length.idxmax())
print("Shortest plot is for season"),
print(df_plots.Length.idxmin())
#What is this show about? (What are the top 3 words used , excluding the #stop words, in all the #seasons combined)
word_sample = list(["struggled", "died"])
word_list = nltk.pos_tag(word_sample)
[wnl.lemmatize(str(word_list[index][0]), pos = word_list[index][1][0].lower()) for index in range(len(word_list))]
# Figure out the stop words
stop = (stopwords.words('english'))
# Tokenize all the plots
df_plots["Tokenized"] = df_plots.Plot.apply(lambda x : nltk.word_tokenize(x.lower()))
# Remove the stop words
df_plots["Filtered"] = df_plots.Tokenized.apply(lambda x : (word for word in x if word not in stop))
# Lemmatize each word
wnl = WordNetLemmatizer()
df_plots["POS"] = df_plots.Filtered.apply(lambda x : nltk.pos_tag(list(x)))
# df_plots["POS"] = df_plots.POS.apply(lambda x : ((word[1] = word[1][0] for word in word_list) for word_list in x))
df_plots["Lemmatized"] = df_plots.POS.apply(lambda x : (wnl.lemmatize(x[index][0], pos = str(x[index][1][0]).lower()) for index in range(len(list(x)))))
#Which Season had the highest screenplay of "Jesse" compared to "Walt"
#Screenplay of Jesse =(Occurences of "Jesse")/(Occurences of "Jesse"+ #Occurences of "Walt")
df_plots.groupby("Season").Tokenized.sum()
df_plots["Share"] = df_plots.groupby("Season").Tokenized.sum().apply(lambda x : float(x.count("jesse") * 100)/float(x.count("jesse") + x.count("walter") + x.count("walt")))
print("The highest times Jesse was mentioned compared to Walter/Walt was in season"),
print(df_plots["Share"].idxmax())
#float(df_plots.Tokenized.sum().count('jesse')) * 100 / #float((df_plots.Tokenized.sum().count('jesse') + #df_plots.Tokenized.sum().count('walt') + #df_plots.Tokenized.sum().count('walter')))