When do I use each ?
Also...is the NLTK lemmatization dependent upon Parts of Speech?
Wouldn't it be more accurate if it was?
Short and dense: http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html
The goal of both stemming and lemmatization is to reduce inflectional forms and sometimes derivationally related forms of a word to a common base form.
However, the two words differ in their flavor. Stemming usually refers to a crude heuristic process that chops off the ends of words in the hope of achieving this goal correctly most of the time, and often includes the removal of derivational affixes. Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma .
From the NLTK docs:
Lemmatization and stemming are special cases of normalization. They identify a canonical representative for a set of related word forms.
Lemmatisation is closely related to stemming. The difference is that a
stemmer operates on a single word without knowledge of the context,
and therefore cannot discriminate between words which have different
meanings depending on part of speech. However, stemmers are typically
easier to implement and run faster, and the reduced accuracy may not
matter for some applications.
For instance:
The word "better" has "good" as its lemma. This link is missed by
stemming, as it requires a dictionary look-up.
The word "walk" is the base form for word "walking", and hence this
is matched in both stemming and lemmatisation.
The word "meeting" can be either the base form of a noun or a form
of a verb ("to meet") depending on the context, e.g., "in our last
meeting" or "We are meeting again tomorrow". Unlike stemming,
lemmatisation can in principle select the appropriate lemma
depending on the context.
Source: https://en.wikipedia.org/wiki/Lemmatisation
Stemming just removes or stems the last few characters of a word, often leading to incorrect meanings and spelling. Lemmatization considers the context and converts the word to its meaningful base form, which is called Lemma. Sometimes, the same word can have multiple different Lemmas. We should identify the Part of Speech (POS) tag for the word in that specific context. Here are the examples to illustrate all the differences and use cases:
If you lemmatize the word 'Caring', it would return 'Care'. If you stem, it would return 'Car' and this is erroneous.
If you lemmatize the word 'Stripes' in verb context, it would return 'Strip'. If you lemmatize it in noun context, it would return 'Stripe'. If you just stem it, it would just return 'Strip'.
You would get same results whether you lemmatize or stem words such as walking, running, swimming... to walk, run, swim etc.
Lemmatization is computationally expensive since it involves look-up tables and what not. If you have large dataset and performance is an issue, go with Stemming. Remember you can also add your own rules to Stemming. If accuracy is paramount and dataset isn't humongous, go with Lemmatization.
There are two aspects to show their differences:
A stemmer will return the stem of a word, which needn't be identical to the morphological root of the word. It usually sufficient that related words map to the same stem,even if the stem is not in itself a valid root, while in lemmatisation, it will return the dictionary form of a word, which must be a valid word.
In lemmatisation, the part of speech of a word should be first determined and the normalisation rules will be different for different part of speech, while the stemmer operates on a single word without knowledge of the context, and therefore cannot discriminate between words which have different meanings depending on part of speech.
Reference http://textminingonline.com/dive-into-nltk-part-iv-stemming-and-lemmatization
The purpose of both stemming and lemmatization is to reduce morphological variation. This is in contrast to the the more general "term conflation" procedures, which may also address lexico-semantic, syntactic, or orthographic variations.
The real difference between stemming and lemmatization is threefold:
Stemming reduces word-forms to (pseudo)stems, whereas lemmatization reduces the word-forms to linguistically valid lemmas. This difference is apparent in languages with more complex morphology, but may be irrelevant for many IR applications;
Lemmatization deals only with inflectional variance, whereas stemming may also deal with derivational variance;
In terms of implementation, lemmatization is usually more sophisticated (especially for morphologically complex languages) and usually requires some sort of lexica. Satisfatory stemming, on the other hand, can be achieved with rather simple rule-based approaches.
Lemmatization may also be backed up by a part-of-speech tagger in order to disambiguate homonyms.
As MYYN pointed out, stemming is the process of removing inflectional and sometimes derivational affixes to a base form that all of the original words are probably related to. Lemmatization is concerned with obtaining the single word that allows you to group together a bunch of inflected forms. This is harder than stemming because it requires taking the context into account (and thus the meaning of the word), while stemming ignores context.
As for when you would use one or the other, it's a matter of how much your application depends on getting the meaning of a word in context correct. If you're doing machine translation, you probably want lemmatization to avoid mistranslating a word. If you're doing information retrieval over a billion documents with 99% of your queries ranging from 1-3 words, you can settle for stemming.
As for NLTK, the WordNetLemmatizer does use the part of speech, though you have to provide it (otherwise it defaults to nouns). Passing it "dove" and "v" yields "dive" while "dove" and "n" yields "dove".
An example-driven explanation on the differenes between lemmatization and stemming:
Lemmatization handles matching “car” to “cars” along
with matching “car” to “automobile”.
Stemming handles matching “car” to “cars” .
Lemmatization implies a broader scope of fuzzy word matching that is
still handled by the same subsystems. It implies certain techniques
for low level processing within the engine, and may also reflect an
engineering preference for terminology.
[...] Taking FAST as an example,
their lemmatization engine handles not only basic word variations like
singular vs. plural, but also thesaurus operators like having “hot”
match “warm”.
This is not to say that other engines don’t handle synonyms, of course
they do, but the low level implementation may be in a different
subsystem than those that handle base stemming.
http://www.ideaeng.com/stemming-lemmatization-0601
Stemming is the process of removing the last few characters of a given word, to obtain a shorter form, even if that form doesn't have any meaning.
Examples,
"beautiful" -> "beauti"
"corpora" -> "corpora"
Stemming can be done very quickly.
Lemmatization on the other hand, is the process of converting the given word into it's base form according to the dictionary meaning of the word.
Examples,
"beautiful" -> "beauty"
"corpora" -> "corpus"
Lemmatization takes more time than stemming.
I think Stemming is a rough hack people use to get all the different forms of the same word down to a base form which need not be a legit word on its own
Something like the Porter Stemmer can uses simple regexes to eliminate common word suffixes
Lemmatization brings a word down to its actual base form which, in the case of irregular verbs, might look nothing like the input word
Something like Morpha which uses FSTs to bring nouns and verbs to their base form
Huang et al. describes the Stemming and Lemmatization as the following. The selection depends upon the problem and computational resource availability.
Stemming identifies the common root form of a word by removing or replacing word suffixes (e.g. “flooding” is stemmed as “flood”), while lemmatization identifies the inflected forms of a word and returns its base form (e.g. “better” is lemmatized as “good”).
Huang, X., Li, Z., Wang, C., & Ning, H. (2020). Identifying disaster related social media for rapid response: a visual-textual fused CNN architecture. International Journal of Digital Earth, 13(9), 1017–1039. https://doi.org/10.1080/17538947.2019.1633425
Stemming and Lemmatization both generate the foundation sort of the inflected words and therefore the only difference is that stem may not be an actual word whereas, lemma is an actual language word.
Stemming follows an algorithm with steps to perform on the words which makes it faster. Whereas, in lemmatization, you used a corpus also to supply lemma which makes it slower than stemming. you furthermore might had to define a parts-of-speech to get the proper lemma.
The above points show that if speed is concentrated then stemming should be used since lemmatizers scan a corpus which consumes time and processing. It depends on the problem you’re working on that decides if stemmers should be used or lemmatizers.
for more info visit the link:
https://towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221
Stemming
is the process of producing morphological variants of a root/base word. Stemming programs are commonly referred to as stemming algorithms or stemmers.
Often when searching text for a certain keyword, it helps if the search returns variations of the word.
For instance, searching for “boat” might also return “boats” and “boating”. Here, “boat” would be the stem for [boat, boater, boating, boats].
Lemmatization
looks beyond word reduction and considers a language’s full vocabulary to apply a morphological analysis to words. The lemma of ‘was’ is ‘be’ and the lemma of ‘mice’ is ‘mouse’.
I did refer this link,
https://towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221
In short, the difference between these algorithms is that only lemmatization includes the meaning of the word in the evaluation. In stemming, only a certain number of letters are cut off from the end of the word to obtain a word stem. The meaning of the word does not play a role in it.
In short:
Lemmatization: uses context to transform words to their
dictionary(base) form also known as Lemma
Stemming: uses the stem of the word, most of the time removing derivational affixes.
source
Related
I need to extract simple triplets from unstructured text. Usually it is of the form noun- verb- noun, so I have tried POS tagging and then extracting nouns and verbs from neighbourhood.
However it leads to lot of cases and gives low accuracy.
Will Syntactic/semantic parsing help in this scenario?
Will ontology based information extraction be more useful?
I expect that syntactic parsing would be the best fit for your scenario. Some trivial template-matching method with POS tags might work, where you find verbs preceded and followed by a single noun, and take the former to be the subject and the latter the object. However, it sounds like you've already tried something like that -- unless your neighborhood extraction ignores word order (which would be a bit silly - you'd be guessing which noun was the word and which was the object, and that's assuming exactly two nouns in each sentence).
Since you're looking for {s, v, o} triplets, chances are you won't need semantic or ontological information. That would be useful if you wanted more information, e.g. agent-patient relations or deeper knowledge extraction.
{s,v,o} is shallow syntactic information, and given that syntactic parsing is considerably more robust and accessible than semantic parsing, that might be your best bet. Syntactic parsing will be sensitive to simple word re-orderings, e.g. "The hamburger was eaten by John." => {John, eat, hamburger}; you'd also be able to specifically handle intransitive and ditransitive verbs, which might be issues for a more naive approach.
Features are used for model training and testing. What are the differences between lexical features and orthographic features in Natural Language Processing? Examples preferred.
I am not aware of such a distinction, and most of the time when people talk about lexical features they talk about using the word itself, in contrast to only using other features, ie its part-of-speech.
Here is an example of a paper that means "whole word orthograph" when they say lexical features
One could venture that orthographic could mean something more abstract than the sequence of characters themselves, for example whether the sequence is capitalized / titlecased / camelcased / etc. But we already have the useful and clearly understood shape feature denomination for that.
As such, I would recommend distinguishing features like this:
lexical features:
whole word, prefix/suffix (various lengths possible), stemmed word, lemmatized word
shape features:
uppercase, titlecase, camelcase, lowercase
grammatical and syntactic features:
POS, part of a noun-phrase, head of a verb phrase, complement of a prepositional phrase, etc...
This is not an exhaustive list of possible features and feature categories, but it might help you categorizing linguistic features in a clearer and more widely-accepted way.
I am doing POS tagging. Given the following tokens in the training set, is it better to consider each token as Word1/POStag and Word2/POStag or consider them as one word that is Word1/Word2/POStag ?
Examples: (the POSTag is not required to be included)
Bard/EMS
Interstate/Johnson
Polo/Ralph
IBC/Donoghue
ISC/Bunker
Bendix/King
mystery/comedy
Jeep/Eagle
B/T
Hawaiian/Japanese
IBM/PC
Princeton/Newport
editing/electronic
Heller/Breene
Davis/Zweig
Fleet/Norstar
a/k/a
1/2
Any suggestion is appreciated.
The examples don't seem to fall into one category with respect to the use of the slash -- a/k/a is a phrase acronym, 1/2 is a number, mystery/comedy indicates something in between the two words, etc.
I feel there is no treatment of the component words that would work for all the cases in question, and therefore the better option is to handle them as unique words. At decoding stage, when the tagger will probably be presented with more previously unseen examples of such words, the decision can often be made based on the context, rather than the word itself.
I'm currently taking a Natural Language Processing course at my University and still confused with some basic concept. I get the definition of POS Tagging from the Foundations of Statistical Natural Language Processing book:
Tagging is the task of labeling (or tagging) each word in a sentence
with its appropriate part of speech. We decide whether each word is a
noun, verb, adjective, or whatever.
But I can't find a definition of Shallow Parsing in the book since it also describe shallow parsing as one of the utilities of POS Tagging. So I began to search the web and found no direct explanation of shallow parsing, but in Wikipedia:
Shallow parsing (also chunking, "light parsing") is an analysis of a sentence which identifies the constituents (noun groups, verbs, verb groups, etc.), but does not specify their internal structure, nor their role in the main sentence.
I frankly don't see the difference, but it may be because of my English or just me not understanding simple basic concept. Can anyone please explain the difference between shallow parsing and POS Tagging? Is shallow parsing often also called Shallow Semantic Parsing?
Thanks before.
POS tagging would give a POS tag to each and every word in the input sentence.
Parsing the sentence (using the stanford pcfg for example) would convert the sentence into a tree whose leaves will hold POS tags (which correspond to words in the sentence), but the rest of the tree would tell you how exactly these these words are joining together to make the overall sentence. For example an adjective and a noun might combine to be a 'Noun Phrase', which might combine with another adjective to form another Noun Phrase (e.g. quick brown fox) (the exact way the pieces combine depends on the parser in question).
You can see how parser output looks like at http://nlp.stanford.edu:8080/parser/index.jsp
A shallow parser or 'chunker' comes somewhere in between these two. A plain POS tagger is really fast but does not give you enough information and a full blown parser is slow and gives you too much. A POS tagger can be thought of as a parser which only returns the bottom-most tier of the parse tree to you. A chunker might be thought of as a parser that returns some other tier of the parse tree to you instead. Sometimes you just need to know that a bunch of words together form a Noun Phrase but don't care about the sub-structure of the tree within those words (i.e. which words are adjectives, determiners, nouns, etc and how do they combine). In such cases you can use a chunker to get exactly the information you need instead of wasting time generating the full parse tree for the sentence.
POS tagging is a process deciding what is the type of every token from a text, e.g. NOUN, VERB, DETERMINER, etc. Token can be word or punctuation.
Meanwhile shallow parsing or chunking is a process dividing a text into syntactically related group.
Pos Tagging output
My/PRP$ dog/NN likes/VBZ his/PRP$ food/NN ./.
Chunking output
[NP My Dog] [VP likes] [NP his food]
The Constraint Grammar framework is illustrative. In its simplest, crudest form, it takes as input POS-tagged text, and adds what you could call Part of Clause tags. For an adjective, for example, it could add #NN> to indicate that it is part of an NP whose head word is to the right.
In POS_tagger, we tag words using a "tagset" like {noun, verb, adj, adv, prob...}
while shallow parser try to define sub-components such as Name Entity and phrases in the sentence like
"I'm currently (taking a Natural (Language Processing course) at (my University)) and (still confused with some basic concept.)"
D. Jurafsky and J. H. Martin say in their book, that shallow parse (partial parse) is a parse that doesn't extract all the possible information from the sentence, but just extract valuable in the specific case information.
Chunking is just a one of the approaches to shallow parsing. As it was mentioned, it extracts only information about basic non-recursive phrases (e.g. verb phrases or noun phrases).
Other approaches, for example, produce flatted parse trees. These trees may contain information about part-of-speech tags, but defer decisions that may require semantic or contextual factors, such as PP attachments, coordination ambiguities, and nominal compound analyses.
So, shallow parse is the parse that produce a partial parse tree. Chunking is an example of such parsing.
I have an algorithm that generates strings based on a list of input words. How do I separate only the strings that sounds like English words? ie. discard RDLO while keeping LORD.
EDIT: To clarify, they do not need to be actual words in the dictionary. They just need to sound like English. For example KEAL would be accepted.
You can build a markov-chain of a huge english text.
Afterwards you can feed words into the markov chain and check how high the probability is that the word is english.
See here: http://en.wikipedia.org/wiki/Markov_chain
At the bottom of the page you can see the markov text generator. What you want is exactly the reverse of it.
In a nutshell: The markov-chain stores for each character the probabilities of which next character will follow. You can extend this idea to two or three characters if you have enough memory.
The easy way with Bayesian filters (Python example from http://sebsauvage.net/python/snyppets/#bayesian)
from reverend.thomas import Bayes
guesser = Bayes()
guesser.train('french','La souris est rentrée dans son trou.')
guesser.train('english','my tailor is rich.')
guesser.train('french','Je ne sais pas si je viendrai demain.')
guesser.train('english','I do not plan to update my website soon.')
>>> print guesser.guess('Jumping out of cliffs it not a good idea.')
[('english', 0.99990000000000001), ('french', 9.9999999999988987e-005)]
>>> print guesser.guess('Demain il fera très probablement chaud.')
[('french', 0.99990000000000001), ('english', 9.9999999999988987e-005)]
You could approach this by tokenizing a candidate string into bigrams—pairs of adjascent letters—and checking each bigram against a table of English bigram frequencies.
Simple: if any bigram is sufficiently low on the frequency table (or outright absent), reject the string as implausible. (String contains a "QZ" bigram? Reject!)
Less simple: calculate the overall plausibility of the whole string in terms of, say, a product of the frequencies of each bigram divided by the mean frequency of a valid English string of that length. This would allow you to both (a) accept a string with an odd low-frequency bigram among otherwise high-frequency bigrams, and (b) reject a string with several individual low-but-not-quite-below-the-threshold bigrams.
Either of those would require some tuning of the threshold(s), the second technique more so than the first.
Doing the same thing with trigrams would likely be more robust, though it'll also likely lead to a somewhat more strict set of "valid" strings. Whether that's a win or not depends on your application.
Bigram and trigram tables based on existing research corpora may be available for free or purchase (I didn't find any freely available but only did a cursory google so far), but you can calculate a bigram or trigram table from yourself from any good-sized corpus of English text. Just crank through each word as a token and tally up each bigram—you might handle this as a hash with a given bigram as the key and an incremented integer counter as the value.
English morphology and English phonetics are (famously!) less than isometric, so this technique might well generate strings that "look" English but present troublesome prounciations. This is another argument for trigrams rather than bigrams—the weirdness produced by analysis of sounds that use several letters in sequence to produce a given phoneme will be reduced if the n-gram spans the whole sound. (Think "plough" or "tsunami", for example.)
It's quite easy to generate English sounding words using a Markov chain. Going backwards is more of a challenge, however. What's the acceptable margin of error for the results? You could always have a list of common letter pairs, triples, etc, and grade them based on that.
You should research "pronounceable" password generators, since they're trying to accomplish the same task.
A Perl solution would be Crypt::PassGen, which you can train with a dictionary (so you could train it to various languages if you need to). It walks through the dictionary and collects statistics on 1, 2, and 3-letter sequences, then builds new "words" based on relative frequencies.
I'd be tempted to run the soundex algorithm over a dictionary of English words and cache the results, then soundex your candidate string and match against the cache.
Depending on performance requirements, you could work out a distance algorithm for soundex codes and accept strings within a certain tolerance.
Soundex is very easy to implement - see Wikipedia for a description of the algorithm.
An example implementation of what you want to do would be:
def soundex(name, len=4):
digits = '01230120022455012623010202'
sndx = ''
fc = ''
for c in name.upper():
if c.isalpha():
if not fc: fc = c
d = digits[ord(c)-ord('A')]
if not sndx or (d != sndx[-1]):
sndx += d
sndx = fc + sndx[1:]
sndx = sndx.replace('0','')
return (sndx + (len * '0'))[:len]
real_words = load_english_dictionary()
soundex_cache = [ soundex(word) for word in real_words ]
if soundex(candidate) in soundex_cache:
print "keep"
else:
print "discard"
Obviously you'll need to provide an implementation of read_english_dictionary.
EDIT: Your example of "KEAL" will be fine, since it has the same soundex code (K400) as "KEEL". You may need to log rejected words and manually verify them if you want to get an idea of failure rate.
Metaphone and Double Metaphone are similar to SOUNDEX, except they may be tuned more toward your goal than SOUNDEX. They're designed to "hash" words based on their phonetic "sound", and are good at doing this for the English language (but not so much other languages and proper names).
One thing to keep in mind with all three algorithms is that they're extremely sensitive to the first letter of your word. For example, if you're trying to figure out if KEAL is English-sounding, you won't find a match to REAL because the initial letters are different.
Do they have to be real English words, or just strings that look like they could be English words?
If they just need to look like possible English words you could do some statistical analysis on some real English texts and work out which combinations of letters occur frequently. Once you've done that you can throw out strings that are too improbable, although some of them may be real words.
Or you could just use a dictionary and reject words that aren't in it (with some allowances for plurals and other variations).
You could compare them to a dictionary (freely available on the internet), but that may be costly in terms of CPU usage. Other than that, I don't know of any other programmatic way to do it.
That sounds like quite an involved task! Off the top of my head, a consonant phoneme needs a vowel either before or after it. Determining what a phoneme is will be quite hard though! You'll probably need to manually write out a list of them. For example, "TR" is ok but not "TD", etc.
I would probably evaluate each word using a SOUNDEX algorithm against a database of english words. If you're doing this on a SQL-server it should be pretty easy to setup a database containing a list of most english words (using a freely available dictionary), and MSSQL server has SOUNDEX implemented as an available search-algorithm.
Obviously you can implement this yourself if you want, in any language - but it might be quite a task.
This way you'd get an evaluation of how much each word sounds like an existing english word, if any, and you could setup some limits for how low you'd want to accept results. You'd probably want to consider how to combine results for multiple words, and you would probably tweak the acceptance-limits based on testing.
I'd suggest looking at the phi test and index of coincidence. http://www.threaded.com/cryptography2.htm
I'd suggest a few simple rules and standard pairs and triplets would be good.
For example, english sounding words tend to follow the pattern of vowel-consonant-vowel, apart from some dipthongs and standard consonant pairs (e.g. th, ie and ei, oo, tr). With a system like that you should strip out almost all words that don't sound like they could be english. You'd find on closer inspection that you will probably strip out a lot of words that do sound like english as well, but you can then start adding rules that allow for a wider range of words and 'train' your algorithm manually.
You won't remove all false negatives (e.g. I don't think you could manage to come up with a rule to include 'rythm' without explicitly coding in that rythm is a word) but it will provide a method of filtering.
I'm also assuming that you want strings that could be english words (they sound reasonable when pronounced) rather than strings that are definitely words with an english meaning.