Added some more words to lexicon dictionary

feature_extraction/features.py

@@ -2,12 +2,9 @@ import feature_extraction.lexicons as lexicons
 from utils.constants import REGEX_CONSTANTS
 
 """ List of supported features for feature extraction from Input String """
-FEATURE_LIST = ['COMPARE', 'CONTRAST', 'RESULT', 'USE', 'IMPORTANT', 'RESEARCH', 'APPROACH',
+FEATURE_LIST = ['COMPARE', 'CONTRAST', 'RESULT', 'INCREASE', 'CHANGE', 'USE', 'PRESENT', 'IMPORTANT', 'RESEARCH',
-                'PUBLIC', 'BEFORE', 'BETTER_SOLUTION', 'PROFESSIONALS', 'CITATION', 'ACRONYM',
+                'APPROACH', 'PUBLIC', 'BEFORE', 'BETTER_SOLUTION', 'PROFESSIONALS', 'MEDICINE', 'MATH', 'CITATION',
-                'CONTAINS_YEAR', 'SEQUENCE', 'REFERENCE', 'PERCENTAGE', 'URL']
+                'ACRONYM', 'CONTAINS_YEAR', 'SEQUENCE', 'REFERENCE', 'PERCENTAGE', 'URL']
-
-""" Features with Regex Pattern Matching - For these features, get the regex pattern from constants"""
-REGEX_FEATURES = ['ACRONYM', 'CONTAINS_YEAR', 'SEQUENCE', 'REFERENCE', 'PERCENTAGE', 'URL']
 
 
 def extract_features_from_text(text: str):
@@ -28,7 +25,7 @@ def extract_features_from_text(text: str):
 
         # If the feature is Regex Pattern Match, get the pattern from :`~feature_extraction.lexicons.ALL_LEXICONS`
         # and match it with the input text
-        if feature in REGEX_FEATURES:
+        if feature in REGEX_CONSTANTS:
             pattern = REGEX_CONSTANTS[feature]
             if bool(pattern.match(text)):
                 text_feature_list.append(feature)
@@ -37,7 +34,7 @@ def extract_features_from_text(text: str):
         # If the feature is not a Regex Pattern Match, then get the list of dictionary words from lexicon dictionary
         word_list = lexicon_dict[feature]
         for word in word_list:
-            if word in text:
+            if word in text.lower():
                 text_feature_list.append(feature)
                 break
 

feature_extraction/lexicons.py

@@ -3,21 +3,32 @@ Dictionary of Lexicons used for Feature Extraction
 """
 ALL_LEXICONS = {
 
-    'COMPARE': ['compar' 'compet', 'evaluat', 'test', 'superior', 'inferior', 'better', 'best', 'worse', 'worst',
+    'COMPARE': ['compar', 'compet', 'evaluat', 'test', 'superior', 'inferior', 'better', 'best', 'good', 'low',
-                'greater', 'larger', 'faster', 'measur'],
+                'worse', 'worst', 'greater', 'larger', 'faster', 'high', 'measur', 'between', 'another', 'similar'],
 
     'CONTRAST': ['contrast', 'different' 'distinct', 'conflict', 'disagree', 'oppose', 'distinguish', 'contrary'],
 
-    'RESULT': ['evidence', 'experiment', 'find', 'progress', 'observation', 'outcome', 'result'],
+    'RESULT': ['estimate', 'evidence', 'experiment', 'find', 'progress', 'observation', 'outcome', 'result', 'performance'],
+
+    'INCREASE': ['increase', 'grow', 'intensify', 'build up', 'explode'],
+
+    'CHANGE': ['adapt', 'adjust', 'augment', 'combine', 'change', 'decrease', 'elaborate', 'expand', 'expand on',
+               'extend', 'derive', 'incorporate', 'increase', 'manipulate', 'modify', 'optimize', 'optimise', 'refine',
+               'render', 'replace', 'revise', 'substitute', 'tailor', 'upgrade', 'grow'],
 
     'USE': ['use', 'using', 'apply', 'applied', 'employ', 'make use', 'utilize', 'implement'],
 
+    'PRESENT': ['describe', 'discuss', 'give', 'introduce', 'note', 'notice', 'present', 'propose', 'recapitulate',
+                'demonstrate', 'remark', 'report', 'say', 'show', 'sketch', 'state', 'suggest', 'figure'],
+
     'IMPORTANT': ['important', 'main', 'key', 'basic', 'central', 'crucial', 'critical', 'essential', 'fundamental',
-                  'great', 'largest', 'major', 'overall', 'primary', 'principle', 'serious', 'substantial', 'ultimate'],
+                  'great', 'largest', 'major', 'overall', 'primary', 'principle', 'serious', 'substantial', 'ultimate',
+                  'significant', 'remarkable', 'noteworthy', 'crucial', 'emerge'],
 
-    'RESEARCH': ['apply', 'analyze', 'characteri', 'formali', 'investigat', 'implement', 'interpret', 'examin',
+    'RESEARCH': ['research', 'paper', 'study', 'studie', 'apply', 'analyze', 'characteri', 'formali', 'investigat',
-                 'observ', 'predict', 'verify', 'work on', 'empirical', 'experiment', 'exploratory', 'ongoing',
+                 'implement', 'interpret', 'examin', 'observ', 'predict', 'verify', 'work on', 'empirical',
-                 'quantitative', 'qualitative', 'preliminary', 'statistical', 'underway'],
+                 'experiment', 'exploratory', 'ongoing', 'quantitative', 'qualitative', 'preliminary', 'statistical',
+                 'knowledge', 'underway', 'discuss', 'reference', 'publish', 'document'],
 
     'APPROACH': ['approach', 'account', 'algorithm', 'analys', 'approach', 'application', 'architecture', 'characteri',
                  'component', 'design', 'extension', 'formali', 'framework', 'implement', 'investigat', 'machine',
@@ -27,15 +38,25 @@ ALL_LEXICONS = {
     'PUBLIC': ['acknowledge', 'admit', 'agree', 'assert', 'claim', 'complain', 'declare', 'deny', 'explain',
                'hint', 'insist', 'mention', 'proclaim', 'promise', 'protest', 'remark', 'reply', 'report', 'say',
                'suggest', 'swear', 'write'],
-    
+
     'BEFORE': ['earlier', 'initial', 'past', 'previous', 'prior'],
-    
+
     'BETTER_SOLUTION': ['boost', 'enhance', 'defeat', 'improve', 'perform better', 'outperform', 'outweigh', 'surpass'],
 
     'PROFESSIONALS': ['colleagues', 'community', 'computer scientists', 'computational linguists', 'discourse analysts',
                       'expert', 'investigators', 'linguists', 'logicians', 'philosophers', 'psycholinguists',
                       'psychologists', 'researchers', 'scholars', 'semanticists', 'scientists'],
 
+    'MEDICINE': ['medicine', 'tissue', 'gene', 'inflammatory', 'mutant', 'neuro', 'digest', 'ortho', 'kinase', 'pneumonia',
+                 'clinical', 'therap', 'kidney', 'receptor', 'cancer', 'synthesis', 'protein', 'syndrom', 'toxin', 'death', 'calcium',
+                 'pharma', 'heart', 'disease', 'vitamin', 'tumor', 'blind', 'symptom', 'medical', 'vaccin', 'molecule',
+                 'biotic', 'patient', 'cells', 'immune', 'blood', 'plasma', 'diagnos', 'neura', 'reproductive', 'plasm', 'drug',
+                 'membrane', 'muscle', 'contagious', 'inflam', 'physician', 'dna', 'genome', 'bacteria', 'cavity', 'antibodies'],
+
+    'MATH': ['matrix', 'gaussian', 'variance', 'radius', 'function', 'comput', 'once', 'twice', 'thrice', 'diagram',
+             'vector', 'rectangle', 'logic', 'amount', 'maxim', 'minim', 'linear', 'magnitude', 'theorem', 'gradient',
+             'exponential', 'complex', 'graph', 'mean', 'equation', 'offset', 'calculat', 'coefficient', 'discrete', 'math'],
+
     'CITATION': ['et al'],  # TODO (for Isaac) :: Write a complex regex for finding Citations in the text
 
 }

testing/eval_testing.py

@@ -1,8 +1,6 @@
 from eval.metrics import f1_score
 import utils.constants as const
 from sklearn.metrics import f1_score as f1
-import os
-from utils.csv import read_csv_file
 
 y_true = ['positive', 'positive', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative']
 y_pred = ['positive', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative', 'positive', 'positive', 'negative', 'negative', 'negative']
@@ -21,12 +19,3 @@ for result in result_list:
 
 print('SK Learn F1 Score (MACRO):: ', f1(y_true, y_pred, ['positive', 'negative'], average='macro'))
 
-
-project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-train_file_path = project_root+'/data/tsv/train.tsv'
-print(train_file_path)
-
-data = read_csv_file(csv_file_path=train_file_path, delimiter='\t')
-for inst in data[:10]:
-    inst.print()

testing/feature_testing.py

@@ -0,0 +1,26 @@
+import os
+from utils.csv import read_csv_file
+
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+train_file_path = project_root+'/data/tsv/train.tsv'
+print(train_file_path)
+
+data = read_csv_file(csv_file_path=train_file_path, delimiter='\t')
+
+i = 0
+for inst in data:
+    if len(inst.features) <= 0:
+        inst.print()
+        i += 1
+print('Data Points without Features :: ', i)
+
+#         tokens = inst.text.split()
+#         for token in tokens:
+#             if token not in feature_dict:
+#                 feature_dict[token] = 1
+#                 continue
+#             feature_dict[token] += 1
+#
+# for key in sorted(feature_dict, key=feature_dict.get, reverse=True):
+#     print(key, ' -> ', feature_dict.get(key))