diff --git a/TIGER/parse_spacy.py b/TIGER/parse_spacy.py
new file mode 100644
index 0000000..de82917
--- /dev/null
+++ b/TIGER/parse_spacy.py
@@ -0,0 +1,87 @@
+import spacy
+from spacy.tokens import Doc
+import logging, sys, time
+from lib.CoNLL_Annotation import CoNLL09_Token
+import my_utils.file_utils as fu
+from germalemma import GermaLemma
+
+
+TIGER_CORPUS = "/home/daza/datasets/TIGER_conll/tiger_release_aug07.corrected.16012013.conll09"
+OUTPUT_FILE = "/home/daza/datasets/TIGER_conll/tiger_spacy_parsed.conllu"
+TEXT_OUTPUT = "/home/daza/datasets/TIGER_conll/tiger_all.txt"
+
+
+class WhitespaceTokenizer(object):
+	def __init__(self, vocab):
+		self.vocab = vocab
+
+	def __call__(self, text):
+		words = text.split(' ')
+		# All tokens 'own' a subsequent space character in this tokenizer
+		spaces = [True] * len(words)
+		return Doc(self.vocab, words=words, spaces=spaces)
+
+
+def get_conll_str(spacy_doc):
+	conll_lines = [] # We want: [ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC]
+	for ix, token in enumerate(spacy_doc):
+		content = (str(ix), token.text, find_germalemma(token.text, token.tag_, token.lemma_), token.pos_, token.tag_, "_", "_", "_", "_", "_")
+		conll_lines.append("\t".join(content))
+	return "\n".join(conll_lines)
+
+
+# def freeling_lemma_lookup():
+# 	dicts_path = "/home/daza/Frameworks/FreeLing/data/de/dictionary/entries/"
+	
+def find_germalemma(word, pos, spacy_lemma):
+	simplify_pos = {"ADJA":"ADJ", "ADJD":"ADJ",
+					"NA":"N", "NE":"N", "NN":"N",
+					"ADV":"ADV", "PAV":"ADV", "PROAV":"ADV", "PAVREL":"ADV", "PWAV":"ADV", "PWAVREL":"ADV",
+					"VAFIN":"V", "VAIMP":"V", "VAINF":"V", "VAPP":"V", "VMFIN":"V", "VMINF":"V",
+					"VMPP":"V", "VVFIN":"V", "VVIMP":"V", "VVINF":"V", "VVIZU":"V","VVPP":"V"
+				}
+	# simplify_pos = {"VERB": "V", "ADV": "ADV", "ADJ": "ADJ", "NOUN":"N", "PROPN": "N"}
+	try:
+		return lemmatizer.find_lemma(word, simplify_pos.get(pos, "UNK"))
+	except:
+		return spacy_lemma
+
+
+if __name__ == "__main__":
+	file_has_next, chunk_ix = True, 0
+	CHUNK_SIZE = 10000
+	
+	# =====================================================================================
+	#                    LOGGING INFO ...
+	# =====================================================================================
+	logger = logging.getLogger(__name__)
+	console_hdlr = logging.StreamHandler(sys.stdout)
+	file_hdlr = logging.FileHandler(filename=f"logs/Parse_Tiger.SpaCy.log")
+	logging.basicConfig(level=logging.INFO, handlers=[console_hdlr, file_hdlr])
+	logger.info(f"Chunking TIGER Corpus in chunks of {CHUNK_SIZE} Sentences")
+	
+	# =====================================================================================
+	#                    POS TAG DOCUMENTS
+	# =====================================================================================
+	spacy_de = spacy.load("de_core_news_lg", disable=["ner", "parser"])
+	spacy_de.tokenizer = WhitespaceTokenizer(spacy_de.vocab) # We won't re-tokenize to respect how the source CoNLL are tokenized!
+	write_out = open(OUTPUT_FILE, "w")
+	lemmatizer = GermaLemma()
+	write_plain = open(TEXT_OUTPUT, "w")
+	
+	start = time.time()
+	total_processed_sents = 0
+	line_generator = fu.file_generator(TIGER_CORPUS)
+	while file_has_next:
+		sents, gld, file_has_next = fu.get_file_text_chunk(line_generator, chunk_size=CHUNK_SIZE, token_class=CoNLL09_Token)
+		total_processed_sents += len(sents)
+		logger.info(f"Already processed {total_processed_sents} sentences...")
+		for doc in spacy_de.pipe(sents, batch_size=1000, n_process=10):
+			conll_str = get_conll_str(doc)
+			write_out.write(conll_str)
+			write_out.write("\n\n")
+			write_plain.write(" ".join([x.text for x in doc])+"\n") # OPTIONAL: This can be commented for efficiency...
+			
+	end = time.time()
+	logger.info(f"Processing File {TIGER_CORPUS} took {(end - start)} seconds!")
+			
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