def main():
# Step 1: Read in training data, initialize dictionary of features with weight 0.
weights = init_suffix_weights("tag_train.dat")
# Step 2: Get the gold tag histories using tagger history generator.py
gold = open("q5_gold", "w")
train_data = open("tag_train.dat", "r")
call(["python", "tagger_history_generator.py", "GOLD"], stdout=gold, stdin=train_data)
# Step 3: Enumerate all possible histories
train_data = open("tag_train.dat", "r")
histories = open("q5_histories", "w")
call(["python", "tagger_history_generator.py", "ENUM"], stdout=histories, stdin=train_data)
# Step 4: Run Perceptron k=4 times.
perceptron(weights, "tag_train.dat", "q5_histories", "q5_gold", 5)
# Step 5: Write the final model out to suffix_tagger.model.
final_model = file("suffix_tagger.model", "w")
for key in weights:
line = key + " " + str(weights[key])
print(line, file=final_model)
# Run model with suffix, tag and bigram features on development data
tag_weights = tagmodel_weights()
weights.update(tag_weights)
weighted_history("tag_dev.dat","q4_histories",weights,"q5_weighted",True,True)
best_tag = open("q5_best", "w")
weighted = open("q5_weighted", "r")
call(["python", "tagger_decoder.py", "HISTORY"], stdout=best_tag, stdin=weighted)
# 4. Save file with word-tag combos
set_tags("tag_dev.dat","q5_best","q5_output")
remove("q5_best")
remove("q5_histories")
remove("q5_weighted")
remove("q5_gold")
def decode():
# read tag.model into a map from feature strings to weights.
weights = tagmodel_weights()
# For each sentence in development data (Steps 1-4):
# 1. Enumerate all possible histories
histories = open("q4_histories", "w")
data = open("tag_dev.dat", "r")
call(["python", "tagger_history_generator.py", "ENUM"], stdout=histories, stdin=data)
# 2. Compute the features for each history and use tag.model to assign a weight to each history
weighted_history("tag_dev.dat","q4_histories",weights,"q4_weighted",True,False)
# 3. Call tagger_decoder.py HISTORY and pipe in the weighted histories to compute the highest scoring tagging.
best_tag = open("q4_best", "w")
weighted = open("q4_weighted", "r")
call(["python", "tagger_decoder.py", "HISTORY"], stdout=best_tag, stdin=weighted)
# 4. Save file with word-tag combos
set_tags("tag_dev.dat","q4_best","q4_output")
remove("q4_best")
remove("q4_weighted")
def main():
# Get suffix, tag and bigram feature vectors generated in quesiton4.py and question5.py
weights = tagmodel_weights()
weights.update(suffix_weights("suffix_tagger.model"))
# Combo 1: Modify certain suffix rules: ============================================
weights_1 = weights
# suffix "ly" is usually ADV; increase weight
weights_1["SUFFIX:ly:ADV"] = float(weights["SUFFIX:ly:ADV"]) + 3
# suffix "ed" is usually VERB; increase weight
weights_1["SUFFIX:ed:VERB"] = float(weights["SUFFIX:ed:VERB"]) + 3
# suffix "ing" is usually VERB; increase weight
weights_1["SUFFIX:ing:VERB"] = float(weights["SUFFIX:ing:VERB"]) + 0.05
# Run model with suffix, tag and bigram features on development data
weighted_history("tag_dev.dat","q4_histories",weights_1,"q6_weighted",True,True)
best_tag = open("q6_best", "w")
weighted = open("q6_weighted", "r")
call(["python", "tagger_decoder.py", "HISTORY"], stdout=best_tag, stdin=weighted)
# 4. Save file with word-tag combos
set_tags("tag_dev.dat","q6_best","q6_output_combo1")
# Combo 2: Modify one bigram rule ==================================================
weights_2 = weights
# bigram "VERB VERB" is often wrong; decrease weight
weights_2["BIGRAM:VERB:VERB"] = -0.5
# Run model with suffix, tag and bigram features on development data
weighted_history("tag_dev.dat","q4_histories",weights_2,"q6_weighted",True,True)
best_tag = open("q6_best", "w")
weighted = open("q6_weighted", "r")
call(["python", "tagger_decoder.py", "HISTORY"], stdout=best_tag, stdin=weighted)
# 4. Save file with word-tag combos
set_tags("tag_dev.dat","q6_best","q6_output_combo2")
# Combo 3: Add content rules: =====================================================
weights_3 = weights
# If a word has a hyphen, tag as ADJ
weights_3["CONTAINS:HYPHEN:ADJ"] = 5
# If word has digits, tag as NUM
weights_3["CONTAINS:DIGIT:NUM"] = 5
weighted_history2("tag_dev.dat","q4_histories",weights_3,"q6_weighted",True,True)
best_tag = open("q6_best", "w")
weighted = open("q6_weighted", "r")
call(["python", "tagger_decoder.py", "HISTORY"], stdout=best_tag, stdin=weighted)
# 4. Save file with word-tag combos
set_tags("tag_dev.dat","q6_best","q6_output_combo3")
remove("q6_best")
# Combo 4: All together now: =====================================================
# suffix "ly" is usually ADV; increase weight
weights["SUFFIX:ly:ADV"] = float(weights["SUFFIX:ly:ADV"]) + 3
# suffix "ed" is usually VERB; increase weight
weights["SUFFIX:ed:VERB"] = float(weights["SUFFIX:ed:VERB"]) + 3
# No use to add to ["SUFFIX:ing:VERB"] feature
# bigram "VERB VERB" is often wrong; decrease weight
# weights["BIGRAM:VERB:VERB"] = -0.5
# If a word has a hyphen, tag as ADJ
weights["CONTAINS:HYPHEN:ADJ"] = 5
# If word has digits, tag as NUM
weights["CONTAINS:DIGIT:NUM"] = 5
weighted_history2("tag_dev.dat","q4_histories",weights,"q6_weighted",True,True)
best_tag = open("q6_best", "w")
weighted = open("q6_weighted", "r")
call(["python", "tagger_decoder.py", "HISTORY"], stdout=best_tag, stdin=weighted)
# 4. Save file with word-tag combos
set_tags("tag_dev.dat","q6_best","q6_output_combo4")
remove("q6_best")
remove("q6_weighted")
remove("q4_histories")
