def ranked_retrieval(queries, collection_table, doc_nums, inverted_index,
stop_words):
ranked_scores = {}
for query_index, query in enumerate(queries):
query_tokens = normalise(remove_stop_words(tokenise(query),
stop_words))
# Convert query into an OR boolean search and use eval to evaluate it
boolean_vectors = []
for token in query_tokens:
boolean_vector = collection_table[token]
boolean_vectors.append('np.array([{}])'.format(
array_to_string(boolean_vector)))
query_eval_string = ' | '.join(boolean_vectors)
query_documents = boolean_search(query_eval_string, doc_nums)
query_scores = []
# Map query_boolean_result to a list of document ids
for doc in query_documents:
score = TFIDF(doc, query_tokens, len(doc_nums), inverted_index)
query_scores.append((doc, score))
# Sort scores for each query on a descending order
query_scores = sorted(query_scores, key=lambda x: x[1], reverse=True)
ranked_scores[query_index + 1] = query_scores
return ranked_scores
def computerelations(self, graph):
for tweet in self:
tokens = preprocess.tokenise(tweet.message)
for token in tokens:
#Does this token look like twitter's @recipient syntax ??
if token and token[0] == '@':
user = token[1:]
self.addrelation(user)
def computerelations(self, graph):
for tweet in self:
tokens = preprocess.tokenise(tweet.message)
for token in tokens:
#Does this token look like twitter's @recipient syntax ??
if token and token[0] == '@':
user = token[1:]
self.addrelation(user)
def get_word_indices_in_text(words, text):
text_token = tokenise(text)
word_indices = []
for word in words:
word_index = text_token.index(word.lower())
word_indices.append(word_index + 1)
return word_indices
def load_docs_from_trec(root):
# Load the provided trec sample xml
root = load_xml(TREC_SAMPLE_FILE, './DOC')
doc_list = []
token_doc_list = []
tokenised_docs = {}
doc_nums = []
test_list = []
for doc in root:
doc_no = doc.find('DOCNO').text
headline = doc.find('HEADLINE').text
text = doc.find('TEXT').text
headline_with_text = headline + ' ' + text
doc_nums.append(doc_no)
doc_list.append(headline_with_text)
test_list.append(tokenise(headline_with_text))
token_doc_list.append(preprocess(headline_with_text))
tokenised_docs[doc_no] = preprocess(headline_with_text)
return doc_nums, token_doc_list, tokenised_docs
def extract_features(filename):
"Open and tokenise the contents of a file."
return tokenise(read_corpus_file(filename))
def prepare_text(filename):
return splitsentences(tokenise(readcorpusfile(filename)))
def preprocess(doc):
return stemming(remove_stop_words(tokenise(doc), stop_words))
def prepare_text(filename):
return splitsentences(tokenise(readcorpusfile(filename)))
