As explained in an earlier post, I am working on a simple method of extracting ‘important words’ from a text-entry. The methods I am using at the moment are frequency distributions and word collocations. I’ve bumped into some issues regarding finetuning my methods. Read on for a short explanation of my approaches, and some issues regarding them.
Frequency Distribution: POS-tagging y/n?
Extracting keywords by frequency distribution is nothing more than counting words and sorting the list of words by occurrence. Before doing this, I filter stopwords from the text entry. The short explanation on how I’m doing this (sourcecode available at github):
» Tokenize the text (using NLTK’s WordPunctTokenizer)
» Lowercase all the words
» ‘Clean’ the list by removing common stopwords from the list (using NLTK’s English stopwords-list)
This is straightforward enough, an example of the results (from the WikiPedia page of ‘Apoptosis‘):
>>> cyttron.wikiGet('Apoptosis') Apoptosis in wikiTxt >>> freqWords(cyttron.wikiTxt,15) ['apoptosis', 'cell', '160', 'apoptotic', 'cells', 'caspase', 'death', '.&#', 'proteins', 'tnf', 'bcl', 'protein', 'also', 'caspases']
Earlier I was thinking about using POS-tagging (Part-Of-Speech tagging to identify word-types) in order to only extract frequently occurring nouns. I figured losing relevant adjectives (such as ‘red’ in red blood cell) could be compensated by the word collocations extraction. POS-tagging the tokenized text, and retrieving only the most frequent nouns results in:
>>> freqNouns(cyttron.wikiTxt,15) ['apoptosis', 'cell', 'caspase', 'death', 'protein', 'tnf', 'pathway', 'activation', 'membrane', 'p53', 'response', 'family', 'gene', 'greek']
My problem here is I’m not sure which is ‘better’ (if any of those two), or if I should maybe use a combination of both. Also, I haven’t decided yet how to handle non-alphabetic words. Initially I planned on using regular expressions to filter non-alphabetic strings, but I figured later that it wouldn’t make sense in my case. In the above example, this would omit retrieving ‘p53′: a tumor suppressor protein (P53), which is very relevant.
With earlier playing around with POS-tagging I noticed the precision was not quite high enough to perform chunk extractions (by looking for specific phrases / grammatical constructions). Extracting only nouns does seem to do quite the job, even if I still miss some and get some false positives.
Word Collocations: Stopword filtering y/n?
Collocation defines a sequence of words or terms that co-occur more often than would be expected by chance. I generate bi- and trigram word collocations, which mean ’2-word strings’ and ’3-word strings’. My issue here is whether or not to use stopword filtering. Here are the results of the word collocation function on the same WikiPedia page, the 1st list being the bigram collocations, the 2nd being the trigrams. Example without stopword filtering:
>>> wordCollo(cyttron.wikiTxt,10,clean=False) ['such as', 'cell death', 'of the', 'due to', 'leads to', 'programmed cell', 'has been', 'bone marrow', 'have been', 'an increase'] ['adp ribose polymerase', 'amino acid composition', 'anatomist walther flemming', 'boston biologist robert', 'break itself down', 'combining forms preceded', 'count falls below', 'german scientist carl', 'homologous antagonist killer', 'mdm2 complexes displaces']
Example with stopword filtering:
>>> wordCollo(cyttron.wikiTxt,10,clean=True) ['cell death', 'programmed cell', 'bone marrow', 'university aberdeen', 'calcium concentration', 'adenovirus e1b', 'british journal', 'citation needed', 'highly conserved', 'nitric oxide'] ['adp ribose polymerase', 'agar gel electrophoresis', 'amino acid composition', 'anatomist walther flemming', 'appearance agar gel', 'awarded sydney brenner', 'boston biologist robert', 'carl vogt first', 'ceases respire aerobically', 'closely enough warrant']
As you can see, lots of garbage in the first example, but still some collocations that do not appear in the cleaned version. Similar to the noun-extraction issue with the previous approach, I wonder if I should choose for one of the two, or combine them.
In other news
Finding Gensim has been a life-saver! Instead of using Difflib to compare two strings, I now use a proper text-similarity metric, namely cosine similarity measurement. I do so by creating a TF-IDF weighted corpus out of the (stopwords-cleaned) descriptions of ontology-terms I use, and calculating the cosine similarity between an input string and each entry in the corpus. Gensim makes this all a breeze to do. An example of the ouput:
>>> wikiGet('alzheimer') alzheimer in wikiTxt >>> descMatch(wikiTxt,5) Label: Alzheimer's disease Similarity: 0.236387 Description: A dementia that results in progressive memory loss, impaired thinking, disorientation, and changes in personality and mood starting in late middle age and leads in advanced cases to a profound decline in cognitive and physical functioning and is marked histologically by the degeneration of brain neurons especially in the cerebral cortex and by the presence of neurofibrillary tangles and plaques containing beta-amyloid. It is characterized by memory lapses, confusion, emotional instability and progressive loss of mental ability. Label: vascular dementia Similarity: 0.192565 Description: A dementia that involves impairments in cognitive function caused by problems in blood vessels that feed the brain. Label: dementia Similarity: 0.157553 Description: A cognitive disease resulting from a loss of brain function affecting memory, thinking, language, judgement and behavior. Label: cognitive disease Similarity: 0.13909 Description: A disease of mental health that affects cognitive functions including memory processing, perception and problem solving. Label: encephalitis Similarity: 0.138719 Description: Encephalitis is a nervous system infectious disease characterized as an acute inflammation of the brain. The usual cause is a viral infection, but bacteria can also cause it. Cases can range from mild to severe. For mild cases, you could have flu-like symptoms. Serious cases can cause severe headache, sudden fever, drowsiness, vomiting, confusion and seizures.
I’m not sure if the similarity numbers it produces indicate I’m doing something wrong (there’s no high similarity), but intuitively I would say the results do make sense.