Unsupervised Natural Language Processing Using Graph Models
Chris Biemann · 2007
In the past, NLP has always been based on the explicit or implicit use of linguistic knowledge. In classical computer linguis-tic applications explicit rule based ap-proaches prevail, while machine learning algorithms use implicit knowledge for generating linguistic knowledge. The question behind this work is: how far can we go in NLP without assuming explicit or implicit linguistic knowledge? How much efforts in annotation and resource building are needed for what level of so-phistication in text processing? This work tries to answer the question by experi-menting with algorithms that do not pre-sume any linguistic knowledge in the system. The claim is that the knowledge needed can largely be acquired by know-ledge-free and unsupervised methods. Here, graph models are employed for rep-resenting language data. A new graph clustering method finds related lexical units, which form word sets on various levels of homogeneity. This is exempli-fied and evaluated on language separation and unsupervised part-of-speech tagging, further applications are discussed. 1