![]() ![]() Score of candidate keyword 'constructing': 1.0 Score of candidate keyword 'corresponding algorithm': 3.5 Score of candidate keyword 'minimal generating set': 7.91666666667 Score of candidate keyword 'construction': 1.0 Score of candidate keyword 'algorithm': 1.5 Score of candidate keyword 'solution': 1.0 Score of candidate keyword 'minimal set': 4.91666666667 Score of candidate keyword 'component': 1.0 Score of candidate keyword 'upper bound': 4.0 Score of candidate keyword 'nonstrict inequations': 4.0 Score of candidate keyword 'strict inequations': 4.0 Score of candidate keyword 'linear diophantine equation': 8.5 Score of candidate keyword 'criterion': 1.0 Score of candidate keyword 'natural number': 4.0 Score of candidate keyword 'linear constraint': 4.5 Score of candidate keyword 'compatibility': 1.0 The phrase scores are calculated by adding individual scores of each of the words Example: "Word of Mouth".īut, for simplicity's sake I will pretend here that such exceptions do not exist.ĭictionary of degree scores for each words under candidate keywords (phrases):ĭefaultdict(, ) There are some exceptions, that is, there are some possible cases where a good keyword candidate may contain Phrases should constitute a group of consecutively occuring words that has no member from stopwords_plus in Set will be used to partition the lemmatized text into phrases. Stopwords-plus constitute the sum total of all stopwords and potential phrase-delimiters. Stopwords to create the final list 'stopwords-plus' which is then converted into a set. Remain which are very bad candidates for being keywords (or part of it).Īn external file constituting a long list of stopwords is loaded and all the words are added with the previous punctuation))Įven if we remove the aforementioned stopwords, still some extremely common nouns, adjectives or gerunds may Punctuations are added to the stopword list too. This is based on the assumption that usually keywords are noun, For example, 'glasses' may be replaced by 'glass'.Īny word from the lemmatized text, which isn't a noun, adjective, or gerund (or a 'foreign word'), is hereĬonsidered as a stopword (non-content). In lemmatization different grammatical counterparts of a word will be replaced by singleīasic lemma. The tokenized text (mainly the nouns and adjectives) is normalized by lemmatization. NLTK is again used for POS tagging the input text. ![]()
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