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Ok, so I've asked a bunch of smaller questions about this project, but I still don't have much confidence in the designs I'm coming up with, so I'm going to ask a question on a broader scale.
I am parsing pre-requisite descriptions for a course catalog. The descriptions almost always follow a certain form, which makes me think I can parse most of them.
From the text, I would like to generate a graph of course pre-requisite relationships. (That part will be easy, after I have parsed the data.)
Some sample inputs and outputs:
"CS 2110" => ("CS", 2110) # 0 "CS 2110 and INFO 3300" => [("CS", 2110), ("INFO", 3300)] # 1 "CS 2110, INFO 3300" => [("CS", 2110), ("INFO", 3300)] # 1 "CS 2110, 3300, 3140" => [("CS", 2110), ("CS", 3300), ("CS", 3140)] # 1 "CS 2110 or INFO 3300" => [[("CS", 2110)], [("INFO", 3300)]] # 2 "MATH 2210, 2230, 2310, or 2940" => [[("MATH", 2210), ("MATH", 2230), ("MATH", 2310)], [("MATH", 2940)]] # 3 If the entire description is just a course, it is output directly.
If the courses are conjoined ("and"), they are all output in the same list
If the course are disjoined ("or"), they are in separate lists
Here, we have both "and" and "or".
One caveat that makes it easier: it appears that the nesting of "and"/"or" phrases is never greater than as shown in example 3.
What is the best way to do this? I started with PLY, but I couldn't figure out how to resolve the reduce/reduce conflicts. The advantage of PLY is that it's easy to manipulate what each parse rule generates:
def p_course(p): 'course : DEPT_CODE COURSE_NUMBER' p[0] = (p[1], int(p[2])) With PyParse, it's less clear how to modify the output of parseString(). I was considering building upon @Alex Martelli's idea of keeping state in an object and building up the output from that, but I'm not sure exactly how that is best done.
def addCourse(self, str, location, tokens): self.result.append((tokens[0][0], tokens[0][1])) def makeCourseList(self, str, location, tokens): dept = tokens[0][0] new_tokens = [(dept, tokens[0][1])] new_tokens.extend((dept, tok) for tok in tokens[1:]) self.result.append(new_tokens) For instance, to handle "or" cases:
def __init__(self): self.result = [] # ... self.statement = (course_data + Optional(OR_CONJ + course_data)).setParseAction(self.disjunctionCourses) def disjunctionCourses(self, str, location, tokens): if len(tokens) == 1: return tokens print "disjunction tokens: %s" % tokens How does disjunctionCourses() know which smaller phrases to disjoin? All it gets is tokens, but what's been parsed so far is stored in result, so how can the function tell which data in result corresponds to which elements of token? I guess I could search through the tokens, then find an element of result with the same data, but that feel convoluted...
Also, there are many descriptions that include misc text, like:
"CS 2110 or permission of instructor" "INFO 3140 or equivalent experience" "PYSCH 2210 and sophomore standing" But it isn't critical that I parse that text.
What's a better way to approach this problem?
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Traditional Methods of Parsing Traditionally, parsing is done by taking a sentence and breaking it down into different parts of speech. The words are placed into distinct grammatical categories, and then the grammatical relationships between the words are identified, allowing the reader to interpret the sentence.
Depending upon how the parse tree is built, parsing techniques are classified into three general categories, namely, universal parsing, top-down parsing, and bottom-up parsing. The most commonly used parsing techniques are top-down parsing and bottom-up parsing.
In order to parse natural language data, researchers must first agree on the grammar to be used. The choice of syntax is affected by both linguistic and computational concerns; for instance some parsing systems use lexical functional grammar, but in general, parsing for grammars of this type is known to be NP-complete.
To parse is to break up a sentence or group of words into separate components, including the definition of each part's function or form. The technical definition implies the same concept. Parsing is used in all high-level programming languages.
def parse(astr): astr=astr.replace(',','') astr=astr.replace('and','') tokens=astr.split() dept=None number=None result=[] option=[] for tok in tokens: if tok=='or': result.append(option) option=[] continue if tok.isalpha(): dept=tok number=None else: number=int(tok) if dept and number: option.append((dept,number)) else: if option: result.append(option) return result if __name__=='__main__': tests=[ ("CS 2110" , [[("CS", 2110)]]), ("CS 2110 and INFO 3300" , [[("CS", 2110), ("INFO", 3300)]]), ("CS 2110, INFO 3300" , [[("CS", 2110), ("INFO", 3300)]]), ("CS 2110, 3300, 3140", [[("CS", 2110), ("CS", 3300), ("CS", 3140)]]), ("CS 2110 or INFO 3300", [[("CS", 2110)], [("INFO", 3300)]]), ("MATH 2210, 2230, 2310, or 2940", [[("MATH", 2210), ("MATH", 2230), ("MATH", 2310)], [("MATH", 2940)]])] for test,answer in tests: result=parse(test) if result==answer: print('GOOD: {0} => {1}'.format(test,answer)) else: print('ERROR: {0} => {1} != {2}'.format(test,result,answer)) break yields
GOOD: CS 2110 => [[('CS', 2110)]] GOOD: CS 2110 and INFO 3300 => [[('CS', 2110), ('INFO', 3300)]] GOOD: CS 2110, INFO 3300 => [[('CS', 2110), ('INFO', 3300)]] GOOD: CS 2110, 3300, 3140 => [[('CS', 2110), ('CS', 3300), ('CS', 3140)]] GOOD: CS 2110 or INFO 3300 => [[('CS', 2110)], [('INFO', 3300)]] GOOD: MATH 2210, 2230, 2310, or 2940 => [[('MATH', 2210), ('MATH', 2230), ('MATH', 2310)], [('MATH', 2940)]] If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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