Update src/ASL_gloss_functions.py

src/ASL_gloss_functions.py

@@ -1,8 +1,8 @@
 # Define a list of question adverbs
 opened_question_adverbs = ["how", "when", "where", "why", "how much", "how many", "how often", "how long", "what", "which", "who", "whose", "whom"]
 
-## time adverbs to be moved at the beginning of ASL Gloss sentences
-time_words = ["yesterday", "today", "tomorrow"]
+# time adverbs to be moved at the beginning of ASL Gloss sentences
+time_words = ["yesterday", "today", "tomorrow", "now", "before", "after", "morning", "afternoon", "evening", "night"]
 
 # ASL glossing rules implemented in functions
 def gloss_word(word):
@@ -36,14 +36,13 @@ def add_time_indicator(gloss_sentence_):
             return f"{word.text.upper()} {gloss_sentence_.replace(word.text.upper(), '').strip()}"
     return gloss_sentence_
 
-## skip stop_words
+# skip stop_words
 def skip_stop_words(word):
     if word.lower() == 'the' or word.lower() == 'a':
         return ''
     else:
         return word
 
-## doc est une liste de tokens
 def question_type(doc):
     try:
         if doc[-1].text == '?':
@@ -52,11 +51,9 @@ def question_type(doc):
             else:
                 return "yes-no-question"
         return None
-    
     except IndexError:
         return None
 
-# 수정된 process_sentence 함수
 def process_sentence(doc):
     nms = {
         "wh-question": "wh-q",
@@ -66,12 +63,19 @@ def process_sentence(doc):
         "car": "CL:3",
         "person": "CL:1"
     }
+    basic_verbs = {
+        "is": "BE",
+        "am": "BE",
+        "are": "BE",
+        "was": "BE",
+        "were": "BE",
+        "be": "BE"
+    }
     
     glossed_sentence = []
     for token in doc:
         word = token.lemma_.lower()
         
-        # 고유명사 처리 추가
         if token.pos_ == "PROPN":
             glossed_word = token.text.upper()
         elif word in ["i", "me"]:
@@ -80,13 +84,14 @@ def process_sentence(doc):
             glossed_word = handle_indexing("YOU", 2)
         elif word in classifiers:
             glossed_word = classifiers[word]
+        elif word in basic_verbs:
+            glossed_word = basic_verbs[word]
         else:
             glossed_word = gloss_word(word)
             
         glossed_word = skip_stop_words(glossed_word)
         glossed_sentence.append(glossed_word)
 
-    # Move time words to beginning
     for gloss in glossed_sentence:
         if gloss.lower() in time_words:
             glossed_sentence.insert(0, glossed_sentence.pop(glossed_sentence.index(gloss)))