I searched online to do bi-gram and unigram text features' extraction, but still didn't find something useful information, can someone tell me what is the difference between them?
For example, if I have a text "I have a lovely dog"
what will happen if I use bi-gram way to do features extraction and to do unigram extraction?
We are trying to teach machine how to do natural language processing. We human can understand language easily but machines cannot so we trying to teach them specific pattern of language. As specific word has meaning but when we combine the words(i.e group of words) than it will be more helpful to understand the meaning.
n-gram is basically set of occurring words within given window so when
n=1 it is Unigram
n=2 it is bigram
n=3 it is trigram and so on
Now suppose machine try to understand the meaning of sentence "I have a lovely dog" then it will split sentences into a specific chunk.
It will consider word one by one which is unigram so each word will be a gram.
"I", "have", "a" , "lovely" , "dog"
It will consider two words at a time so it will be biagram so each two adjacent words will be biagram
"I have" , "have a" , "a lovely" , "lovely dog"
So like this machine will split sentences into small group of words to understand its meaning
Example: Consider the sentence "I ate banana".
In Unigram we assume that the occurrence of each word is independent of its previous word.
Hence each word becomes a gram(feature) here.
For unigram, we will get 3 features - 'I', 'ate', 'banana' and all 3 are independent of each other. Although this is not the case in real languages.
In Bigram we assume that each occurrence of each word depends only on its previous word. Hence two words are counted as one gram(feature) here.
For bigram, we will get 2 features - 'I ate' and 'ate banana'.
This makes sense since the model will learn that 'banana' comes after 'ate' and not the other way around.
Similarly, we can have trigram.......n-gram.
Related
If a single query from the user contains multiple questions belonging to different categories, how can they be identified, split and parsed?
Eg -
User - what is the weather now and tell me my next meeting
Parser - {:weather => "what is the weather", :schedule => "tell me my next meeting"}
Parser identifies the parts of sentences where the question belongs to two different categories
User - show me hotels in san francisco for tomorrow that are less than $300 but not less than $200 are pet friendly have a gym and a pool with 3 or 4 stars staying for 2 nights and dont include anything that doesnt have wifi
Parser - {:hotels => ["show me hotels in san francisco",
"for tomorrow", "less than $300 but not less than $200",
"pet friendly have a gym and a pool",
"with 3 or 4 stars", "staying for 2 nights", "with wifi"]}
Parser identifies the question belonging to only one category but has additional steps for fine tuning the answer and created an array ordered according to the steps to take
From what I can understand this requires a sentence segmenter, multi-label classifier and co-reference resolution
But the sentence segementer I have come across depend heavily on grammar, punctuations.
Multi-label classifiers, like a good trained naive bayes classifier works in most cases but since they are multi-label, most times output multiple categories for sentences which clearly belong to one class. Depending solely on the array outputs to check the labels present would fail.
If used a multi-class classifier, that is also good to check the array output of probable categories but obviously they dont tell the different parts of the sentence much accurately, much less in what fashion to proceed with the next step.
As a first step, how can I tune sentence segmenter to correctly split the sentence without any strict grammar rules. Good accuracy of this would help a lot in classification.
As a first step, how can I tune sentence segmenter to correctly split the sentence without any strict grammar rules.
Instead of doing this I'd suggest you use the parse-tree directly (either dependency parser, or constituency parse).
Here I'm showing the output of the dependency parse and you can see that the two segments are separated via a "CONJ" arrow:
(from here: http://deagol.cs.illinois.edu:8080/)
Another solution I'd give try is ClausIE:
https://gate.d5.mpi-inf.mpg.de/ClausIEGate/ClausIEGate?inputtext=what+is+the+weather+now+and+tell+me+my+next+meeting++&processCcAllVerbs=true&processCcNonVerbs=true&type=true&go=Extract
If you want something for segmentation that doesn't depend on grammar heavily, then chunking comes to mind. In the NLTK book there is a fragment on that. The approach authors take here depends only on part of speech tags.
BTW Jurafsky and Martin's 3rd ed of Speech and Language processing contains information on chunking in the parsing chapter, and it also contains a chapters on information retrieval nad chatbots.
I'm new to Named Entity Recognition and I'm having some trouble understanding what/how features are used for this task.
Some papers I've read so far mention features used, but don't really explain them, for example in
Introduction to the CoNLL-2003 Shared Task:Language-Independent Named Entity Recognition, the following features are mentioned:
Main features used by the the sixteen systems that participated in the
CoNLL-2003 shared task sorted by performance on the English test data.
Aff: affix information (n-grams); bag: bag of words; cas: global case
information; chu: chunk tags; doc: global document information; gaz:
gazetteers; lex: lexical features; ort: orthographic information; pat:
orthographic patterns (like Aa0); pos: part-of-speech tags; pre:
previously predicted NE tags; quo: flag signing that the word is
between quotes; tri: trigger words.
I'm a bit confused by some of these, however. For example:
isn't bag of words supposed to be a method to generate features (one for each word)? How can BOW itself be a feature? Or does this simply mean we have a feature for each word as in BOW, besides all the other features mentioned?
how can a gazetteer be a feature?
how can POS tags exactly be used as features ? Don't we have a POS tag for each word? Isn't each object/instance a "text"?
what is global document information?
what is the feature trigger words?
I think all I need here is to just to look at an example table with each of these features as columns and see their values to understand how they really work, but so far I've failed to find an easy to read dataset.
Could someone please clarify or point me to some explanation or example of these features being used?
Here's a shot at some answers (and by the way the terminology on all this stuff is super overloaded).
isn't bag of words supposed to be a method to generate features (one for each word)? How can BOW itself be a feature? Or does this simply mean we have a feature for each word as in BOW, besides all the other features mentioned?
how can a gazetteer be a feature?
In my experience BOW Feature Extraction is used to produce word features out of sentences. So IMO BOW is not one feature, it is a method of generating features out of a sentence (or a block of text you are using). Uning NGrams can help with accounting for sequence, but BOW features amount to unordered bags of strings.
how can POS tags exactly be used as features ? Don't we have a POS tag for each word?
POS Tags are used as features because they can help with "word sense disambiguation" (at least on a theoretical level). For instance, the word "May" can be a name of a person or a month of a year or a poorly capitalized conjugated verb, but the POS tag can be the feature that differentiates that fact. And yes, you can get a POS tag for each word, but unless you explicitly use those tags in your "feature space" then the words themselves have no idea what they are in terms of their POS.
Isn't each object/instance a "text"?
If you mean what I think you mean, then this is true only if you have extracted object-instance "pairs" and stored them as features (an array of them derived from a string of tokens).
what is global document information?
I perceive this one to mean as such: Most NLP tasks function on a sentence. Global document information is data from all the surrounding text in the entire document. For instance, if you are trying to extract geographic placenames but disambiguate them, and you find the word Paris, which one is it? Well if France is mentioned 5 sentences above, that could increase the likelihood of it being Paris France rather than Paris Texas or worst case, the person Paris Hilton. It's also really important in what is called "coreference resolution", which is when you correlate a name to a pronoun reference (mapping a name mention to "he" or "she" etc).
what is the feature trigger words?
Trigger words are specific tokens or sequences that have high reliability as a stand alone thing to have a specific meaning. For instance, in sentiment analysis, curse words with exclamation marks often indicate negativity. There can be many permutations of this.
Anyway, my answers here are not perfect, and are prone to all manner of problems in human epistemology and inter-subjectivity, but those are the way I've been thinking about this things over the years I've been trying to solve problems with NLP.
Hopefully someone else will chime in, especially if I'm way off.
You should probably keep in mind that NER classify each word/token separately from features that are internal or external clues. Internal clues takes into account the word itself (morphology as uppercase letters, is the token present in a dedicated lexicon, POS) and external ones relies on contextual information (previous and next word, document features).
isn't bag of words supposed to be a method to generate features (one
for each word)? How can BOW itself be a feature? Or does this simply
mean we have a feature for each word as in BOW, besides all the other
features mentioned?
Yes, BOW generates one feature per word, with sometimes feature selection methods that reduces the number features taken into account (e.g. minimum frequency of words)
how can a gazetteer be a feature?
Gazetteer may also generate one feature per word, but in most cases it does enrich data, by labelling words or multi-word expressions (as full proper names). It is an ambiguous step: "Georges Washington" will lead to two features: entire "Georges Washington" as a celebrity and "Washington" as a city.
how can POS tags exactly be used as features ? Don't we have a POS tag
for each word? Isn't each object/instance a "text"?
For classifiers, each instance is a word. This is why sequence labelling (e.g. CRF) methods are used: they allow to leverage previous words and next words as additional contextual features to classify the current word. Labelling a text is done as a process relying on the most likely NE types for each word in the sequence.
what is global document information?
This could be metadata (e.g. date, author), topics (full text categorization), coreference, etc.
what is the feature trigger words?
Triggers are external clues, contextual patterns that help disambiguation. For instance "Mr" will be used as a feature that strongly suggest that the following tokens would be a person.
I recently implemented a NER system in python and I found the following features helpful:
character-level ngrams (using CountVectorizer)
previous word features and labels (i.e. context)
viterbi or beam-search on label sequence probability
part of speech (pos), word-length, word-count, is_capitalized, is_stopword
I am working on developing a tool for language identification of a given text i.e. given a sample text, identify the language (for e.g. English, Swedish, German, etc.) it is written in.
Now the strategy I have decided to follow (based on a few references I have gathered) are as follows -
a) Create a character n-gram model (The value of n is decided based on certain heuristics and computations)
b) Use a machine learning classifier(such as naive bayes) to predict the language of the given text.
Now, the doubt I have is - Is creating a character N-gram model necessary. As in, what disadvantage does a simple bag of words strategy have i.e. if I use all the words possible in the respective language to create a prediction model, what could be the possible cases where it would fail.
The reason why this doubt arose was the fact that any reference document/research paper I've come across states that language identification is a very difficult task. However, just using this strategy of using the words in the language seems to be a simple task.
EDIT: One reason why N-gram should be preferred is to make the model robust even if there are typos as stated here. Can anyone point out more?
if I use all the words possible in the respective language to create a prediction model, what could be the possible cases where it would fail
Pretty much the same cases were a character n-gram model would fail. The problem is that you're not going to find appropriate statistics for all possible words.(*) Character n-gram statistics are easier to accumulate and more robust, even for text without typos: words in a language tend to follow the same spelling patterns. E.g. had you not found statistics for the Dutch word "uitbuiken" (a pretty rare word), then the occurrence of the n-grams "uit", "bui" and "uik" would still be strong indicators of this being Dutch.
(*) In agglutinative languages such as Turkish, new words can be formed by stringing morphemes together and the number of possible words is immense. Check the first few chapters of Jurafsky and Martin, or any undergraduate linguistics text, for interesting discussions on the possible number of words per language.
Cavnar and Trenkle proposed a very simple yet efficient approach using character n-grams of variable length. Maybe you should try to implement it first and move to a more complex ML approach if C&T approach doesn't meet your requirements.
Basically, the idea is to build a language model using only the X (e.g. X = 300) most frequent n-grams of variable length (e.g. 1 <= N <= 5). Doing so, you are very likely to capture most functional words/morphemes of the considered language... without any prior linguistic knowledge on that language!
Why would you choose character n-grams over a BoW approach? I think the notion of character n-gram is pretty straightforward and apply to every written language. Word, is a much much complex notion which greatly differ from one language to another (consider languages with almost no spacing marks).
Reference: http://odur.let.rug.nl/~vannoord/TextCat/textcat.pdf
The performance really depends on your expected input. If you will be classifying multi-paragraph text all in one language, a functional words list (which your "bag of words" with pruning of hapaxes will quickly approximate) might well serve you perfectly, and could work better than n-grams.
There is significant overlap between individual words -- "of" could be Dutch or English; "and" is very common in English but also means "duck" in the Scandinavian languages, etc. But given enough input data, overlaps for individual stop words will not confuse your algorithm very often.
My anecdotal evidence is from using libtextcat on the Reuters multilingual newswire corpus. Many of the telegrams contain a lot of proper names, loan words etc. which throw off the n-gram classifier a lot of the time; whereas just examining the stop words would (in my humble estimation) produce much more stable results.
On the other hand, if you need to identify short, telegraphic utterances which might not be in your dictionary, a dictionary-based approach is obviously flawed. Note that many North European languages have very productive word formation by free compounding -- you see words like "tandborstställbrist" and "yhdyssanatauti" being coined left and right (and Finnish has agglutination on top -- "yhdyssanataudittomienkinkohan") which simply cannot be expected to be in a dictionary until somebody decides to use them.
Wanted some ideas about building a tool which can scan text sentences (written in english language) and build a keyword rank, based on the most occurrences of words or phrases within the texts.
This would be very similar to the twitter trends wherin twitter detects and reports the top 10 words within the tweets.
I have identified the high level steps in the algorithm as follows
Scan the text and remove all the common , frequent words ( such as, "the" , "is" , "are", "what" , "at" etc..)
Add the remaining words to a hashmap. If the word is already in the map then increment its count.
To get the top 10 words , iterate through the hashmap and find out the top 10 counts.
Step 2 and 3 are straightforward but I do not know in step 1 how do I detect the important words within a text and segregate them from the common words (prepositions, conjunctions etc )
Also if I want to track phrases what could be the approach ?
For example if I have a text saying "This honey is very good"
I might want to track "honey" and "good" but I may also want to track the phrases "very good" or "honey is very good"
Any suggestions would be greatly appreciated.
Thanks in advance
For detecting phrases, I suggest to use chunker. You can use one provided by NLP tool like OpenNLP or Stanford CoreNLP.
NOTE
honey is very good is not a phrase. It is clause. very good is a phrase.
In Information Retrieval System, those common word are called Stop Words.
Actually, your step 1 would be quite similar to step 3 in the sense that you may want to constitute an absolute database of the most common words in the English language in the first place. Such a list is available easily on the internet (Wikipedia even has an article referencing the 100 most common words in the English language.) You can store those words in a hashmap and while scanning your text contents just ignore the common tokens.
If you don't trust Wikipedia and the already existing listing for common words, you can build your own database. For that purpose, just scan thousands of tweets (the more the better) and make your own frequency chart.
You're facing an n-gram-like problem.
Do not reinvent the wheel. What you seem to be wanting to do has been done thousands of times, just use existing libs or pieces of code (check the External Links section of the n-gram Wikipedia page.)
Check out the NLTK library. It has code that does number one two and three:
1 Removing common words can be done using stopwords or a stemmer
2,3 getting the most common words can be done with FreqDist
Second you can use tools from Stanford NLP for tracking your text
Summary
I am trying to design a heuristic for matching up sentences in a translation (from the original language to the translated language) and would like guidance and tips. Perhaps there is a heuristic that already does something similar? So given two text files, I would like to be able to match up the sentences (so I can pick out a sentence and say this is the translation of that sentence).
Details
The input text would be translated novels. So I do not expect the translations to be literal, although, using something like google translate might be a good way to test the accuracy of the heuristic.
To help me, I have a library that will gloss the contents of the translated text and give me the definitions of the words in the sentence. Other things I know:
Chapters and order are preserved; I know that the first sentence in chapter three will match with the first sentence in chapter three of the translation (Note, this is not strictly true; the first sentence might match up with the first two sentences, or even the second sentence)
I can calculate the overall size (characters, sentences, paragraphs); which could give me an idea of the average difference in sentence size (for example, the translation might be 30% longer).
Looking at the some books I have, the translated version has about 30% more sentences than the original text.
Implementation
(if it matters)
I am planning to do this in Java - but I am not that fussed - any language will do.
I am not greatly concerned about speed.
I guess to to be sure of the matches, some user feedback might be required. Like saying "Yes, this sentence definitely matches with that sentence." This would give the heuristic some more ground to stand on. This would mean that the user would need a little proficiency in the languages.
Background
(for those interested)
The reason I want to make this is that I want it to assist with my foreign language study. I am studying Japanese and find it hard to find "good" material (where "good" is defined by what I like). There are already tools to do something similar with subtitles from videos (an easier task - using the timing information of the video). But nothing, as far as I know, for texts.
There are tools called "sentence aligners" used in NLP research that does exactly what you want.
I advise hunalign:
http://mokk.bme.hu/resources/hunalign/
and MS sentence aligner:
http://research.microsoft.com/en-us/downloads/aafd5dcf-4dcc-49b2-8a22-f7055113e656/
Both are quite OK, but remember that nothing is perfect. Sentences that are too hard to be aligned will be dropped and some sentences may be wrongly aligned.