second solution

run.py

@@ -0,0 +1,50 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+
+with open('train/train.tsv', 'r', encoding='utf8') as f:
+    train_data = f.readlines()
+
+with open('train/meta.tsv', 'r', encoding='utf8') as f:
+    expected_years = f.readlines()
+
+for i, expected in enumerate(expected_years):
+    expected_years[i] = expected.split('\t')[5]
+#
+vectorizer = TfidfVectorizer(token_pattern=r"\b[a-zA-Z]+\b|[0-9]{4}[\.]?[a-z]{0,3}[\.]?")
+train = vectorizer.fit_transform(train_data)
+model = LinearRegression()
+model.fit(train, expected_years)
+
+with open('dev-0/in.tsv', 'r', encoding='utf8') as f:
+    dev_0 = f.readlines()
+
+# prediction on test1 data
+dev_0 = vectorizer.transform(dev_0)
+predicted_dev_0 = model.predict(dev_0)
+
+with open('dev-0/out.tsv', 'wt') as f:
+    for p in predicted_dev_0:
+        f.write(str(p) + '\n')
+
+with open('dev-1/in.tsv', 'r', encoding='utf8') as f:
+    dev_1 = f.readlines()
+
+# prediction on test2 data
+dev_1 = vectorizer.transform(dev_1)
+predicted_dev_1 = model.predict(dev_1)
+
+with open('dev-1/out.tsv', 'wt') as f:
+    for p in predicted_dev_1:
+        f.write(str(p) + '\n')
+
+with open('test-A/in.tsv', 'r', encoding='utf8') as f:
+    test_A = f.readlines()
+
+test_A = vectorizer.transform(test_A)
+predicted_test_A = model.predict(test_A)
+
+with open('test-A/out.tsv', 'wt') as f:
+    for p in predicted_test_A:
+        f.write(str(p) + '\n')
+
+print("finished")