I think part of my issue has to do with spaCy and part has to do with not understanding the most elegant way to work within python itself.
I am uploading a txt file in python, tokenizing it into sentences and then tokenizing that into words with nltk:
sent_text = nltk.sent_tokenize(text)
tokenized_text = [nltk.word_tokenize(x) for x in sent_text]
That gives me a list of lists, where each list within the main list is a sentence of tokenized words. So far so good.
I then run it through SpaCy:
text = nlp(unicode(tokenized_text))
Still a list of lists, same thing, but it has all the SpaCy info.
This is where I'm hitting a block. Basically what I want to do is, for each sentence, only retain the nouns, verbs, and adjectives, and within those, also get rid of auxiliaries and conjunctions. I was able to do this earlier by creating a new empty list and appending only what I want:
sent11 = []
for token in sent1:
if (token.pos_ == 'NOUN' or token.pos_ == 'VERB' or token.pos_ =='ADJ') and (token.dep_ != 'aux') and (token.dep_ != 'conj'):
sent11.append(token)
This works fine for a single sentence, but I don't want to be doing it for every single sentence in a book-length text.
Then, once I have these new lists (or whatever the best way to do it is) containing only the pieces I want, I want to use the "similarity" function of SpaCy to determine which sentence is closest semantically to some other, much shorter text that I've done the same stripping of everything but nouns, adj, verbs, etc to.
I've got it working when comparing one single sentence to another by using:
sent1.similarity(sent2)
So I guess my questions are
1) What is the best way to turn a list of lists into a list of lists that only contain the pieces I want?
and
2) How do I cycle through this new list of lists and compare each one to a separate sentence and return the sentence that is most semantically similar (using the vectors that SpaCy comes with)?
You're asking a bunch of questions here so I'm going to try to break them down.
Is nearly duplicating a book-length amount of text by appending each word to a list bad?
How can one eliminate or remove elements of a list efficiently?
How can one compare a sentence to each sentence in the book where each sentence is a list and the book is a list of sentences.
Answers:
Generally yes, but on a modern system it isn't a big deal. Books are text which are probably just UTF-8 characters if English, otherwise they might be Unicode. A UTF-8 character is a byte and even a long book such as War and Peace comes out to under 3.3 Mb. If you are using chrome, firefox, or IE to view this page your computer has more than enough memory to fit a few copies of it into ram.
In python you can't really.
You can do removal using:
l = [1,2,3,4]
del l[-2]
print(l)
[1,2,4]
but in the background python is copying every element of that list over one. It is not recommended for large lists. Instead using a dequeue which implements itself as a doublely-linked-list has a bit of extra overhead but allows for efficient removal of elements in the middle.
If memory is an issue then you can also use generators wherever possible. For example you could probably change:
tokenized_text = [nltk.word_tokenize(x) for x in sent_text]
which creates a list that contains tokens of the entire book, with
tokenized_text = (nltk.word_tokenize(x) for x in sent_text)
which creates a generator that yields tokens of the entire book. Generators have almost no memory overhead and instead compute the next element as they go.
I'm not familiar with SpaCy, and while the question fits on SO you're unlikely to get good answers about specific libraries here.
From the looks of it you can just do something like:
best_match = None
best_similarity_value = 0
for token in parsed_tokenized_text:
similarity = token.similarity(sent2)
if similarity > best_similarity_value:
best_similarity_value = similarity
best_match = token
And if you wanted to check against multiple sentences (non-consecutive) then you could put an outer loop that goes through those:
for sent2 in other_token_list:
Related
Got an interesting one, and can't come up with any solid ideas, so thought maybe someone else may have done something similar.
I want to be able to identify strings of letters in a longer sentence that are not words and remove them. Essentially things like kuashdixbkjshakd
Everything annoyingly is in lowercase which makes it more difficult, but since I only care about English, I'm essentially looking for the opposite of consonant clusters, groups of them that don't make phonetically pronounceable sounds.
Has anyone heard of/done something like this before?
EDIT: this is what ChatGpt tells me
It is difficult to provide a comprehensive list of combinations of consonants that have never appeared in a word in the English language. The English language is a dynamic and evolving language, and new words are being created all the time. Additionally, there are many regional and dialectal variations of the language, which can result in different sets of words being used in different parts of the world.
It is also worth noting that the frequency of use of a particular combination of consonants in the English language is difficult to quantify, as the existing literature on the subject is limited. The best way to determine the frequency of use of a particular combination of consonants would be to analyze a large corpus of written or spoken English.
In general, most combinations of consonants are used in some words in the English language, but some combinations of consonants may be relatively rare. Some examples of relatively rare combinations of consonants in English include "xh", "xw", "ckq", and "cqu". However, it is still possible that some words with these combinations of consonants exist.
You could try to pass every single word inside the sentence to a function that checks wether the word is listed inside a dictionary. There is a good number of dictionary text files on GitHub. To speed up the process: use a hash map :)
You could also use an auto-corretion API or a library.
Algorithm to combine both methods:
Run sentence through auto correction
Run every word through dictionary
Delete words that aren't listed in the dictionary
This could remove typos and words that are non-existent.
You could train a simple model on sequences of characters which are permitted in the language(s) you want to support, and then flag any which contain sequences which are not in the training data.
The LangId language detector in SpamAssassin implements the Cavnar & Trenkle language-identification algorithm which basically uses a sliding window over the text and examines the adjacent 1 to 5 characters at each position. So from the training data "abracadabra" you would get
a 5
ab 2
abr 2
abra 2
abrac 1
b 2
br 2
bra 2
brac 1
braca 1
:
With enough data, you could build a model which identifies unusual patterns (my suggestion would be to try a window size of 3 or smaller for a start, and train it on several human languages from, say, Wikipedia) but it's hard to predict how precise exactly this will be.
SpamAssassin is written in Perl and it should not be hard to extract the language identification module.
As an alternative, there is a library called libtextcat which you can run standalone from C code if you like. The language identification in LibreOffice uses a fork which they adapted to use Unicode specifically, I believe (though it's been a while since I last looked at that).
Following Cavnar & Trenkle, all of these truncate the collected data to a few hundred patterns; you would probably want to extend this to cover up to all the 3-grams you find in your training data at least.
Perhaps see also Gertjan van Noord's link collection: https://www.let.rug.nl/vannoord/TextCat/
Depending on your test data, you could still get false positives e.g. on peculiar Internet domain names and long abbreviations. Tweak the limits for what you want to flag - I would think that GmbH should be okay even if you didn't train on German, but something like 7 or more letters long should probably be flagged and manually inspected.
This will match words with more than 5 consonants (you probably want "y" to not be considered a consonant, but it's up to you):
\b[a-z]*[b-z&&[^aeiouy]]{6}[a-z]*\b
See live demo.
5 was chosen because I believe witchcraft has the longest chain of consonants of any English word. You could dial back "6" in the regex to say 5 or even 4 if you don't mind matching some outliers.
I want to get a specific digit based on the right string.
This stretch of string is in body2.txt
string = "<li>3 <span class='text-info'>quartos</span></li><li>1 <span class='text-info'>suíte</span></li><li>96<span class='text-info'>Área Útil (m²)</span></li>"
with open("body2.txt", 'r') as f:
area = re.compile(r'</span></li><li>(\d+)<span class="text-info">Área Útil')
area = area.findall(f.read())
print(area)
output: []
expected output: 96
You have a quote mismatch. Note carefully the difference between 'text-info' and "text-info" in your example string and in your compiled regex. IIRC escaping quotes in raw strings is a bit of a pain in Python (if it's even possible?), but string concatenation sidesteps the issue handily.
area = re.compile(r'</span></li><li>(\d+)<span class='"'"'text-info'"'"'>Área Útil')
Focusing on the quotes, this is concatenating the strings '...class', "'", 'text-info', "'", and '>.... The rule there is that if you want a single quote ' in a single-quote raw string you instead write '"'"' and try to ignore Turing turning in his grave. I haven't tested the performance, but I think it might behave much like '...class' + "'" + 'text-info' + "'" + '>.... If that's the case, there is a bunch of copying happening behind the scenes, and that strategy has a quadratic runtime in the number of pieces being concatenated (assuming they're roughly the same size and otherwise generally nice for such an analysis). You'd be better off with nearly any other strategy (such as ''.join(...) or using triple quoted raw strings r'''...'''). It might not be a problem though. Benchmark your solution and see if it's good enough before messing with alternatives.
As one of the comments mentioned, you probably want to be parsing the HTML with something more powerful than regex. Regex cannot properly parse arbitrary HTML since it can't parse arbitrarily nested structures. There are plenty of libraries to make the job easier though and handle all of the bracket matching and string munging for you so that you can focus on a high-level description of exactly the data you want. I'm a fan of lxml. Without putting a ton of time into it, something like the following would be roughly equivalent to what you're doing.
from lxml import html
with open("body2.txt", 'r') as f:
tree = html.fromstring(f.read())
area = tree.xpath("//li[contains(span/text(), 'Área Útil')]/text()")
print(area)
The html.fromstring() method parses your data as html. The tree.xpath method uses xpath syntax to query that parsed tree. Roughly speaking it means the following:
// Arbitrarily far down in the tree
li A list node
[*] Satisfying whatever property is in the square brackets
contains(span/text(), 'Área Útil') The li node needs to have a span/text() node containing the text 'Área Útil'
/text() We want any text that is an immediate child of the root li we're describing.
I'm working on a pretty small amount of text here and don't know what your document structure is in the general case. You could add or change any of those properties to better describe the exact document you're parsing. When you inspect an element, any modern browser is able to generate a decent xpath expression to pick out exactly the element you're inspecting. Supposing this snippet came from a larger document I would imagine that functionality would be a time saver for you.
This will get the right digits no matter how / what form the target is in.
Capture group 1 contains the digits.
r"(\d*)\s*<span(?=\s)(?=(?:[^>\"']|\"[^\"]*\"|'[^']*')*?\sclass\s*=\s*(?:(['\"])\s*text-info\s*\2))\s+(?=((?:\"[\S\s]*?\"|'[\S\s]*?'|[^>]?)+>))\3\s*Área\s+Útil"
https://regex101.com/r/pMATkj/1
I want a dictionary of English words available, to pick random english words. I have a dictionary text file that I downloaded form the internet which has almost 1 million words, what's the best way to go about using this list in Clojure, given that most of the time I'll only need 1 randomly selected word?
Edit:
To answer the comments, this is for some tests which I may turn into load tests which is why I want a decent number of random words and I guess access speed is the most important thing. I do not want to use a database for this. I originally thought of a dictionary just because that's the first thing that popped into my mind but I think a random sequence of letters and numbers would be good enough, perhaps I will just use a UUID as a string.
Read all the words into a Vector and then call rand-nth , e.g.
(rand-nth all-words)
rand-nth uses the nth function on the underlying data structure and Clojure Vectors have log32N performance for index based retrieval.
Edit: This is assuming that it is for a test environment as you described in your question. A more memory efficient method would be to use RandomAccessFile and seek to a random location in the file of words, read until you find the first word delimiter (e.g. comma, EOL) and then read the following bytes until the next delimiter which will give you a random word.
As of right now, I decided to take a dictionary and iterate through the entire thing. Every time I see a newline, I make a string containing from that newline to the next newline, then I do string.find() to see if that English word is somewhere in there. This takes a VERY long time, each word taking about 1/2-1/4 a second to verify.
It is working perfectly, but I need to check thousands of words a second. I can run several windows, which doesn't affect the speed (Multithreading), but it still only checks like 10 a second. (I need thousands)
I'm currently writing code to pre-compile a large array containing every word in the English language, which should speed it up a lot, but still not get the speed I want. There has to be a better way to do this.
The strings I'm checking will look like this:
"hithisisastringthatmustbechecked"
but most of them contained complete garbage, just random letters.
I can't check for impossible compinations of letters, because that string would be thrown out because of the 'tm', in between 'thatmust'.
You can speed up the search by employing the Knuth–Morris–Pratt (KMP) algorithm.
Go through every dictionary word, and build a search table for it. You need to do it only once. Now your search for individual words will proceed at faster pace, because the "false starts" will be eliminated.
There are a lot of strategies for doing this quickly.
Idea 1
Take the string you are searching and make a copy of each possible substring beginning at some column and continuing through the whole string. Then store each one in an array indexed by the letter it begins with. (If a letter is used twice store the longer substring.
So the array looks like this:
a - substr[0] = "astringthatmustbechecked"
b - substr[1] = "bechecked"
c - substr[2] = "checked"
d - substr[3] = "d"
e - substr[4] = "echecked"
f - substr[5] = null // since there is no 'f' in it
... and so forth
Then, for each word in the dictionary, search in the array element indicated by its first letter. This limits the amount of stuff that has to be searched. Plus you can't ever find a word beginning with, say 'r', anywhere before the first 'r' in the string. And some words won't even do a search if the letter isn't in there at all.
Idea 2
Expand upon that idea by noting the longest word in the dictionary and get rid of letters from those strings in the arrays that are longer than that distance away.
So you have this in the array:
a - substr[0] = "astringthatmustbechecked"
But if the longest word in the list is 5 letters, there is no need to keep any more than:
a - substr[0] = "astri"
If the letter is present several times you have to keep more letters. So this one has to keep the whole string because the "e" keeps showing up less than 5 letters apart.
e - substr[4] = "echecked"
You can expand upon this by using the longest words starting with any particular letter when condensing the strings.
Idea 3
This has nothing to do with 1 and 2. Its an idea that you could use instead.
You can turn the dictionary into a sort of regular expression stored in a linked data structure. It is possible to write the regular expression too and then apply it.
Assume these are the words in the dictionary:
arun
bob
bill
billy
body
jose
Build this sort of linked structure. (Its a binary tree, really, represented in such a way that I can explain how to use it.)
a -> r -> u -> n -> *
|
b -> i -> l -> l -> *
| | |
| o -> b -> * y -> *
| |
| d -> y -> *
|
j -> o -> s -> e -> *
The arrows denote a letter that has to follow another letter. So "r" has to be after an "a" or it can't match.
The lines going down denote an option. You have the "a or b or j" possible letters and then the "i or o" possible letters after the "b".
The regular expression looks sort of like: /(arun)|(b(ill(y+))|(o(b|dy)))|(jose)/ (though I might have slipped a paren). This gives the gist of creating it as a regex.
Once you build this structure, you apply it to your string starting at the first column. Try to run the match by checking for the alternatives and if one matches, more forward tentatively and try the letter after the arrow and its alternatives. If you reach the star/asterisk, it matches. If you run out of alternatives, including backtracking, you move to the next column.
This is a lot of work but can, sometimes, be handy.
Side note I built one of these some time back by writing a program that wrote the code that ran the algorithm directly instead of having code looking at the binary tree data structure.
Think of each set of vertical bar options being a switch statement against a particular character column and each arrow turning into a nesting. If there is only one option, you don't need a full switch statement, just an if.
That was some fast character matching and really handy for some reason that eludes me today.
How about a Bloom Filter?
A Bloom filter, conceived by Burton Howard Bloom in 1970 is a
space-efficient probabilistic data structure that is used to test
whether an element is a member of a set. False positive matches are
possible, but false negatives are not; i.e. a query returns either
"inside set (may be wrong)" or "definitely not in set". Elements can
be added to the set, but not removed (though this can be addressed
with a "counting" filter). The more elements that are added to the
set, the larger the probability of false positives.
The approach could work as follows: you create the set of words that you want to check against (this is done only once), and then you can quickly run the "in/not-in" check for every sub-string. If the outcome is "not-in", you are safe to continue (Bloom filters do not give false negatives). If the outcome is "in", you then run your more sophisticated check to confirm (Bloom filters can give false positives).
It is my understanding that some spell-checkers rely on bloom filters to quickly test whether your latest word belongs to the dictionary of known words.
This code was modified from How to split text without spaces into list of words?:
from math import log
words = open("english125k.txt").read().split()
wordcost = dict((k, log((i+1)*log(len(words)))) for i,k in enumerate(words))
maxword = max(len(x) for x in words)
def infer_spaces(s):
"""Uses dynamic programming to infer the location of spaces in a string
without spaces."""
# Find the best match for the i first characters, assuming cost has
# been built for the i-1 first characters.
# Returns a pair (match_cost, match_length).
def best_match(i):
candidates = enumerate(reversed(cost[max(0, i-maxword):i]))
return min((c + wordcost.get(s[i-k-1:i], 9e999), k+1) for k,c in candidates)
# Build the cost array.
cost = [0]
for i in range(1,len(s)+1):
c,k = best_match(i)
cost.append(c)
# Backtrack to recover the minimal-cost string.
costsum = 0
i = len(s)
while i>0:
c,k = best_match(i)
assert c == cost[i]
costsum += c
i -= k
return costsum
Using the same dictionary of that answer and testing your string outputs
>>> infer_spaces("hithisisastringthatmustbechecked")
294.99768817854056
The trick here is finding out what threshold you can use, keeping in mind that using smaller words makes the cost higher (if the algorithm can't find any usable word, it returns inf, since it would split everything to single-letter words).
In theory, I think you should be able to train a Markov model and use that to decide if a string is probably a sentence or probably garbage. There's another question about doing this to recognize words, not sentences: How do I determine if a random string sounds like English?
The only difference for training on sentences is that your probability tables will be a bit larger. In my experience, though, a modern desktop computer has more than enough RAM to handle Markov matrices unless you are training on the entire Library of Congress (which is unnecessary- even 5 or so books by different authors should be enough for very accurate classification).
Since your sentences are mashed together without clear word boundaries, it's a bit tricky, but the good news is that the Markov model doesn't care about words, just about what follows what. So, you can make it ignore spaces, by first stripping all spaces from your training data. If you were going to use Alice in Wonderland as your training text, the first paragraph would, perhaps, look like so:
alicewasbeginningtogetverytiredofsittingbyhersisteronthebankandofhavingnothingtodoonceortwiceshehadpeepedintothebookhersisterwasreadingbutithadnopicturesorconversationsinitandwhatistheuseofabookthoughtalicewithoutpicturesorconversation
It looks weird, but as far as a Markov model is concerned, it's a trivial difference from the classical implementation.
I see that you are concerned about time: Training may take a few minutes (assuming you have already compiled gold standard "sentences" and "random scrambled strings" texts). You only need to train once, you can easily save the "trained" model to disk and reuse it for subsequent runs by loading from disk, which may take a few seconds. Making a call on a string would take a trivially small number of floating point multiplications to get a probability, so after you finish training it, it should be very fast.
I am writing a program which will tokenize the input text depending upon some specific rules. I am using C++ for this.
Rules
Letter 'a' should be converted to token 'V-A'
Letter 'p' should be converted to token 'C-PA'
Letter 'pp' should be converted to token 'C-PPA'
Letter 'u' should be converted to token 'V-U'
This is just a sample and in real time I have around 500+ rules like this. If I am providing input as 'appu', it should tokenize like 'V-A + C-PPA + V-U'. I have implemented an algorithm for doing this and wanted to make sure that I am doing the right thing.
Algorithm
All rules will be kept in a XML file with the corresponding mapping to the token. Something like
<rules>
<rule pattern="a" token="V-A" />
<rule pattern="p" token="C-PA" />
<rule pattern="pp" token="C-PPA" />
<rule pattern="u" token="V-U" />
</rules>
1 - When the application starts, read this xml file and keep the values in a 'std::map'. This will be available until the end of the application(singleton pattern implementation).
2 - Iterate the input text characters. For each character, look for a match. If found, become more greedy and look for more matches by taking the next characters from the input text. Do this until we are getting a no match. So for the input text 'appu', first look for a match for 'a'. If found, try to get more match by taking the next character from the input text. So it will try to match 'ap' and found no matches. So it just returns.
3 - Replace the letter 'a' from input text as we got a token for it.
4 - Repeat step 2 and 3 with the remaining characters in the input text.
Here is a more simple explanation of the steps
input-text = 'appu'
tokens-generated=''
// First iteration
character-to-match = 'a'
pattern-found = true
// since pattern found, going recursive and check for more matches
character-to-match = 'ap'
pattern-found = false
tokens-generated = 'V-A'
// since no match found for 'ap', taking the first success and replacing it from input text
input-text = 'ppu'
// second iteration
character-to-match = 'p'
pattern-found = true
// since pattern found, going recursive and check for more matches
character-to-match = 'pp'
pattern-found = true
// since pattern found, going recursive and check for more matches
character-to-match = 'ppu'
pattern-found = false
tokens-generated = 'V-A + C-PPA'
// since no match found for 'ppu', taking the first success and replacing it from input text
input-text = 'u'
// third iteration
character-to-match = 'u'
pattern-found = true
tokens-generated = 'V-A + C-PPA + V-U' // we'r done!
Questions
1 - Is this algorithm looks fine for this problem or is there a better way to address this problem?
2 - If this is the right method, std::map is a good choice here? Or do I need to create my own key/value container?
3 - Is there a library available which can tokenize string like the above?
Any help would be appreciated
:)
So you're going through all of the tokens in your map looking for matches? You might as well use a list or array, there; it's going to be an inefficient search regardless.
A much more efficient way of finding just the tokens suitable for starting or continuing a match would be to store them as a trie. A lookup of a letter there would give you a sub-trie which contains only the tokens which have that letter as the first letter, and then you just continue searching downward as far as you can go.
Edit: let me explain this a little further.
First, I should explain that I'm not familiar with these the C++ std::map, beyond the name, which makes this a perfect example of why one learns the theory of this stuff as well as than details of particular libraries in particular programming languages: unless that library is badly misusing the name "map" (which is rather unlikely), the name itself tells me a lot about the characteristics of the data structure. I know, for example, that there's going to be a function that, given a single key and the map, will very efficiently search for and return the value associated with that key, and that there's also likely a function that will give you a list/array/whatever of all of the keys, which you could search yourself using your own code.
My interpretation of your data structure is that you have a map where the keys are what you call a pattern, those being a list (or array, or something of that nature) of characters, and the values are tokens. Thus, you can, given a full pattern, quickly find the token associated with it.
Unfortunately, while such a map is a good match to converting your XML input format to a internal data structure, it's not a good match to the searches you need to do. Note that you're not looking up entire patterns, but the first character of a pattern, producing a set of possible tokens, followed by a lookup of the second character of a pattern from within the set of patterns produced by that first lookup, and so on.
So what you really need is not a single map, but maps of maps of maps, each keyed by a single character. A lookup of "p" on the top level should give you a new map, with two keys: p, producing the C-PPA token, and "anything else", producing the C-PA token. This is effectively a trie data structure.
Does this make sense?
It may help if you start out by writing the parsing code first, in this manner: imagine someone else will write the functions to do the lookups you need, and he's a really good programmer and can do pretty much any magic that you want. Writing the parsing code, concentrate on making that as simple and clean as possible, creating whatever interface using these arbitrary functions you need (while not getting trivial and replacing the whole thing with one function!). Now you can look at the lookup functions you ended up with, and that tells you how you need to access your data structure, which will lead you to the type of data structure you need. Once you've figured that out, you can then work out how to load it up.
This method will work - I'm not sure that it is efficient, but it should work.
I would use the standard std::map rather than your own system.
There are tools like lex (or flex) that can be used for this. The issue would be whether you can regenerate the lexical analyzer that it would construct when the XML specification changes. If the XML specification does not change often, you may be able to use tools such as lex to do the scanning and mapping more easily. If the XML specification can change at the whim of those using the program, then lex is probably less appropriate.
There are some caveats - notably that both lex and flex generate C code, rather than C++.
I would also consider looking at pattern matching technology - the sort of stuff that egrep in particular uses. This has the merit of being something that can be handled at runtime (because egrep does it all the time). Or you could go for a scripting language - Perl, Python, ... Or you could consider something like PCRE (Perl Compatible Regular Expressions) library.
Better yet, if you're going to use the boost library, there's always the Boost tokenizer library -> http://www.boost.org/doc/libs/1_39_0/libs/tokenizer/index.html
You could use a regex (perhaps the boost::regex library). If all of the patterns are just strings of letters, a regex like "(a|p|pp|u)" would find a greedy match. So:
Run a regex_search using the above pattern to locate the next match
Plug the match-text into your std::map to get the replace-text.
Print the non-matched consumed input and replace-text to your output, then repeat 1 on the remaining input.
And done.
It may seem a bit complicated, but the most efficient way to do that is to use a graph to represent a state-chart. At first, i thought boost.statechart would help, but i figured it wasn't really appropriate. This method can be more efficient that using a simple std::map IF there are many rules, the number of possible characters is limited and the length of the text to read is quite high.
So anyway, using a simple graph :
0) create graph with "start" vertex
1) read xml configuration file and create vertices when needed (transition from one "set of characters" (eg "pp") to an additional one (eg "ppa")). Inside each vertex, store a transition table to the next vertices. If "key text" is complete, mark vertex as final and store the resulting text
2) now read text and interpret it using the graph. Start at the "start" vertex. ( * ) Use table to interpret one character and to jump to new vertex. If no new vertex has been selected, an error can be issued. Otherwise, if new vertex is final, print the resulting text and jump back to start vertex. Go back to (*) until there is no more text to interpret.
You could use boost.graph to represent the graph, but i think it is overly complex for what you need. Make your own custom representation.