diff --git a/markov.py b/markov.py
index b46a9de..ddb69c7 100644
--- a/markov.py
+++ b/markov.py
@@ -22,72 +22,64 @@ import os
 import spacy
 
 parser = argparse.ArgumentParser(description='Generate a novel using Markov chains.')
-parser.add_argument('word_file', help='file used for word selection')
-parser.add_argument('pos_file', help='file used to build part-of-speech Markov chain')
+parser.add_argument('input', nargs='+', help='used to construct Markov transition matrix')
 parser.add_argument('-s', '--seed', type=int, help='seed for random number generator')
 args = parser.parse_args()
 
 nlp = spacy.load('en_core_web_sm')
 
-seed = args.seed or 12345
-
-rng = np.random.default_rng(seed)
-
-words_text = ''
-with open(args.word_file, mode='r') as f:
-    words_text = f.read()
-
-words_doc = nlp(words_text)
+rng = np.random.default_rng(args.seed or 12345)
 
 words = {}
-for sent in words_doc.sents:
-    cnt = 0
-    for token in sent:
-        if token.pos_ in ('SPACE', 'PUNCT', 'X'):
-            continue
+edges = []
 
-        cnt += 1
+input_text = ''
+for infile in args.input:
+    with open(infile, mode='r') as f:
+        input_text = f.read()
 
-        word = token.text
+    doc = nlp(input_text)
 
-        state = f'{token.tag_},{token.dep_}'
+    for sent in doc.sents:
+        cnt = 0
+        for token in sent:
+            if token.pos_ in ('SPACE', 'PUNCT', 'X'):
+                continue
 
-        if state in words:
-            words[state].append(word)
-        else:
-            words[state] = [word]
+            cnt += 1
 
-        state = f'{token.tag_},{token.dep_},{str(cnt)}'
+            word = token.text
 
-        if state in words:
-            words[state].append(word)
-        else:
-            words[state] = [word]
+            state = f'{token.tag_},{token.dep_}'
 
+            if state in words:
+                words[state].append(word)
+            else:
+                words[state] = [word]
 
-pos_text = ''
-with open(args.pos_file, mode='r') as f:
-    pos_text = f.read()
+            state = f'{token.tag_},{token.dep_},{str(cnt)}'
 
-pos_doc = nlp(pos_text)
+            if state in words:
+                words[state].append(word)
+            else:
+                words[state] = [word]
 
-edges = []
-for sent in pos_doc.sents:
-    curr_state = 'START'
+    for sent in doc.sents:
+        curr_state = 'START'
 
-    cnt = 0
-    for token in sent:
-        if token.pos_ in ('SPACE', 'PUNCT', 'X'):
-            continue
+        cnt = 0
+        for token in sent:
+            if token.pos_ in ('SPACE', 'PUNCT', 'X'):
+                continue
 
-        cnt += 1
-        next_state = f'{token.tag_},{token.dep_},{str(cnt)}'
+            cnt += 1
+            next_state = f'{token.tag_},{token.dep_},{str(cnt)}'
 
-        edges.append((curr_state, next_state))
+            edges.append((curr_state, next_state))
 
-        curr_state = next_state
+            curr_state = next_state
 
-    edges.append((curr_state, 'STOP'))
+        edges.append((curr_state, 'STOP'))
 
 transitions = {}
 for edge in edges: