I am trying to tag a HTML page full of space-separated numbers like "5320412185 5320412184 5320412189..." to observe how the tagger behaves with numbers. I'm using english-left3words-distsim.tagger in the constructor. I'm observing on the console that most of the numbers are tagged as CD but at times there are also numbers getting tagged as NN. I searched on the FAQ page of nlp.stanford.edu but I couldn't find this there. Can anyone help me in understanding this?
I don't know if I should need to mention this: I'm feeding each number separately to the tagger by splitting the huge input(1045000 numbers!) based on space-delimiter.
From Part-of-Speech Tagging Guidelines for the Penn Treebank Project (3rd Revision)
Sometimes, it is unclear whether one is cardinal number or a noun. In general, it should be tagged as a
cardinal number (CD) even when its sense is not clearly that of a numeral.
EXAMPLE: one/CD of the best reasons
But if it could be pluralized or modified by an adjective in a particular context, it is a common noun (NN).
EXAMPLE: the only (good) one/NN of its kind
(cf. the only (good) ones/NNS of their kind)
In the collocation another one, one should also be tagged as a common noun (NN).
Hyphenated fractions one-half, three-fourths, seven-eighths, one-and-a-half, seven-and-three-eighths should
be tagged as adjectives (JJ) when they are prenominal modifiers, but as adverbs (RB) if they could be
replaced by double or twice.
For further reading: http://repository.upenn.edu/cgi/viewcontent.cgi?article=1603&context=cis_reports
Related
I have some unique codes that are generated from strings (ex: website host names) in various independent components of my application.
These codes are meant to be used by machines only so i would like to keep them as short as possible.
The below algorithm would be applied to every word in the string. The output words would be concatenated with a dash to generate the unique code.
The current algorithm I have used:
- Skip word if length is less than 6
- Leave first character as is
- Remove every wowel in the word from the second character onwards
architectural digest eu => archtctrl-dgst-eu
arizona foothills magazine => arzn-fthlls-mgzn
Is there a better way to shorten an English word leaving it as recognisable as possible to a human reader?
The output should be deterministic and produce the same shortened version whenever it is run on the same input.
A good algorithm should also minimise the number of clashes for similarly spelt words.
I have some unique codes that are generated from strings
I am afraid that is not true. There are many English words that will reduce to the same 'code word' when stripped of their vowels. For example, 'leaving' -> 'living' Given, this is fairly rare, it could still cause issues.
How important is it that these 'code words' remain human-readable if as you say, they are meant to be used by machines only? If its not that important, I'd suggest looking into some simpler compression algorithms like Huffman Coding or LZW Compression. Then if the user needs to see the translation of the code word, just uncompress it.
If you must keep it human-readable, I'm not sure that there is much more you can do to shorten it. You could take a look at specific latin + greek roots, and determine if you can shorten those any more by hand, and then just substitute those out automatically.
Alternatively, you could turn to a phonetic approach. Automatically search the pronunciation of the word, and then see if that is any shorter (or itself can be compressed, taking 'cee' to 'C', or 'kay' to 'K'). This would be much more time and CPU intensive, but its still an option if you really, really need short but yet readable codes.
What you're generating sounds like what's called a "slug". There are many libraries to handle this for blogs or site generators that should suit your purposes. Here's a usage example from a Python library called slugify:
txt = "___This is a test ---"
r = slugify(txt)
self.assertEqual(r, "this-is-a-test")
Slug libraries generally work like this:
replacing non-ascii linguistic characters via a mapping (ex: 影師嗎 -> ying-shi-ma)
replace accented latin letters with ascii equivalents via a mapping (ex: C'est déjà l'été. -> c-est-deja-l-ete)
remove beginning and trailing spaces/punctuation
convert remaining spaces and punctuation to dashes, collapsing multiple dashes in a row to a single dash
If you want to make slugs shorter you could remove vowels or, more simply, use a maximum length.
I have some text generated by some lousy OCR software.
The output contains mixture of words and space-separated characters, which should have been grouped into words. For example,
Expr e s s i o n Syntax
S u m m a r y o f T e r minology
should have been
Expression Syntax
Summary of Terminology
What algorithms can group characters into words?
If I program in Python, C#, Java, C or C++, what libraries provide the implementation of the algorithms?
Thanks.
Minimal approach:
In your input, remove the space before any single letter words. Mark the final words created as part of this somehow (prefix them with a symbol not in the input, for example).
Get a dictionary of English words, sorted longest to shortest.
For each marked word in your input, find the longest match and break that off as a word. Repeat on the characters left over in the original "word" until there's nothing left over. (In the case where there's no match just leave it alone.)
More sophisticated, overkill approach:
The problem of splitting words without spaces is a real-world problem in languages commonly written without spaces, such as Chinese and Japanese. I'm familiar with Japanese so I'll mainly speak with reference to that.
Typical approaches use a dictionary and a sequence model. The model is trained to learn transition properties between labels - part of speech tagging, combined with the dictionary, is used to figure out the relative likelihood of different potential places to split words. Then the most likely sequence of splits for a whole sentence is solved for using (for example) the Viterbi algorithm.
Creating a system like this is almost certainly overkill if you're just cleaning OCR data, but if you're interested it may be worth looking into.
A sample case where the more sophisticated approach will work and the simple one won't:
input: Playforthefunofit
simple output: Play forth efunofit (forth is longer than for)
sophistiated output: Play for the fun of it (forth efunofit is a low-frequency - that is, unnatural - transition, while for the is not)
You can work around the issue with the simple approach to some extent by adding common short-word sequences to your dictionary as units. For example, add forthe as a dictionary word, and split it in a post processing step.
Hope that helps - good luck!
I've been thinking of if this was created already but image a function that can validate a string and determine if it's a word or not. eg
print(validateWord("Hello")) --> true
print(validateWord("Haloe")) --> true (may not be a real word but follows the standards of placements of vowels and such)
print(validateWord("sewxdw")) --> false
I'm not asking for code, I would just like knowledge of if this exists already and a wiki post to this algorithm would be nice if it did.
What you want is a hidden Markov model, trained on the words in a corpus of English (or whatever language you are interested in). You can then score putative words for whether the model likes them or not. It will only disallow actually disallowed combinations like "jx" but it should give a low score to unlikely candidates.
You might have better luck trying to break up the text into phoneme symbols (th, ae qu, ph etc) first rather than writing a model that uses raw letters.
I'm writing a program that makes word declension for Polish language. In this language stems can vary in some cases (because of palatalization or mobile/fleeting e and other effects).
For example, we have word "karzeł" and it is basic dictionary form of word. It's stem is also 'karzeł'. But genitive form of this word is "karła" and stem is "karł". We can see here that 'e' dissapeared and 'rz' changes to 'r'.
Another example:
'uzda' -> stem 'uzd'
'uździe' -> stem 'uździ'
Alternation: 'zd' -> 'ździ'
I'd like to store in dictionary only basic form of stem ('karzeł' and 'uzd') and when I'll put in my program stem 'karł' or 'uździ' it will find proper basic stems. Alternations takes place only at the end of stem and contains maximum 4 letters of it.
Is there any algorithms that could do that? Levensthein distance treats all letters equally so if I type word 'barzeł' then the distance to stem 'karzeł' will be less than to stem 'karł'.
I thought also about neural networks but I'm not sure how to encode words (give each stem variation different id?).
Another idea is to write algorith which makes something like reversed alternation and creates set of possible stems and try to find them in dictionary.
I would like to highlight that I only want store basic form of stem and everything else makes on the fly.
First of all, I remember seeing a number of projects on Polish morphology around. So I would look at them first, before starting one of your own.
Regarding Levenshtein, as Pierre correctly noted in the comment, the distance function can be customized. And it should be. Let me put it this way: think of Levenshtein not as an algorithm of and in itself, but as a solution to a specific error model. First he suggests a model which says that when you are typing a word every letter can be either dropped or replaced by another one due to some random process (fingers not pressing the right keys). Then, his algorithm is just a generator of maximum likelihood solutions under this model. The more errors you allow, the smaller is the probability of this sequence of errors actually happening, the bigger is the score.
You (implicitly) state a very different hypothesis, though. That Polish stems may have certain flexibility at the end (some linguistic process that you do not fully understand within this framework). Then, when you strip your suffix (or something that looks like one), there are three options:
1) there is a chance that what you have here is just a different form of a stem you have stored in your dictionary, or
2) it is a completely different stem, or
3) you've stripped your suffix improperly and what you have is not stem at all.
You can heuristically estimate these probabilities by looking at how many letters in the beginning of the supposed stem match some dictionary entries, for example (how to find these entries is a related but different question). And then you can pick the guess that is the most plausible according to your metric/heuristic.
Now, note that you can use any algorithm to find the candidates in the dictionary. Including the Levenshtein algorithm - as long as you are reasonably sure that the right ones will be picked up. But obviously you are better off writing your own dictionary search algorithm that follows your own metric or emulates it. For example, by giving the biggest/prohibitive cost to the change of letters in the beginning of the word and reducing it as you go towards the end.
This script is compling without errors in play.golang.org: http://play.golang.org/p/Hlr-IAc_1f
But when I run in on my machine, much longer than I expect happens with nothing happening in the terminal.
What I am trying to build is a PartOfSpeech Tagger.
I think the longest part is loading lexicon.txt into a map and then comparing each word with every word there to see if it has already been tagged in the lexicon. The lexicon only contains verbs. But doesn't every word need to be checked to see if it is a verb.
The larger problem is that I don't know how to determine if a word is a verb with an easy heuristic like adverbs, adjectives, etc.
(Quoting):
I don't know how to determine if a word is a verb with an easy heuristic like adverbs, adjectives, etc.
I can't speak to any issues in your Go implementation, but I'll address the larger problem of POS tagging in general. It sounds like you're attempting to build a rule-based unigram tagger. To elaborate a bit on those terms:
"unigram" means you're considering each word in the sentence separately. Note that a unigram tagger is inherently limited, in that it cannot disambiguate words which can take on multiple POS tags. E.g., should you tag 'fish' as a noun or a verb? Is 'last' a verb or an adverb?
"rule-based" means exactly what it sounds like: a set of rules to determine the tag for each word. Rule-based tagging is limited in a different way - it requires considerable development effort to assemble a ruleset that will handle a reasonable portion of the ambiguity in common language. This effort might be appropriate if you're working in a language for which we don't have good training resources, but in most common languages, we now have enough tagged text to train high-accuracy tagging models.
State-of-the-art for POS tagging is above 97% accuracy on well-formed newswire text (accuracy on less formal genres is naturally lower). A rule-based tagger will probably perform considerably worse (you'll have to determine the accuracy level needed to meet your requirements). If you want to continue down the rule-based path, I'd recommend reading this tutorial. The code is based on Haskell, but it will help you learn the concepts and issues in rule-based tagging.
That said, I'd strongly recommend you look at other tagging methods. I mentioned the weaknesses of unigram tagging. Related approaches would be 'bigram', meaning that we consider the previous word when tagging word n, 'trigram' (usually the previous 2 words, or the previous word, the current word, and the following word); more generally, 'n-gram' refers to considering a sequence of n words (often, a sliding window around the word we're currently tagging). That context can help us disambiguate 'fish', 'last', 'flies', etc.
E.g., in
We fish
we probably want to tag fish as a verb, whereas in
ate fish
it's certainly a noun.
The NLTK tutorial might be a good reference here. An solid n-gram tagger should get you above 90% accuracy; likely above 95% (again on newswire text).
More sophisticated methods (known as 'structured inference') consider the entire tag sequence as a whole. That is, instead of trying to predict the most probable tag for each word separately, they attempt to predict the most probable sequence of tags for the entire input sequence. Structured inference is of course more difficult to implement and train, but will usually improve accuracy vs. n-gram approaches. If you want to read up on this area, I suggest Sutton and McCallum's excellent introduction.
You've got a large array argument in this function:
func stringInArray(a string, list [214]string) bool{
for _, b := range list{
if b == a{
return true;
}
}
return false
}
The array of stopwords gets copied each time you call this function.
Mostly in Go, you should uses slices rather than arrays most of the time. Change the definition of this to be list []string and define stopWords as a slice rather than an array:
stopWords := []string{
"and", "or", ...
}
Probably an even better approach would be to build a map of the stopWords:
isStopWord := map[string]bool{}
for _, sw := range stopWords {
isStopWord[sw] = true
}
and then you can check if a word is a stopword quickly:
if isStopWord[word] { ... }