We propose an algorithm to automatically induce the morphology of inflectional languages using only text corpora and no human input . Our algorithm combines cues from orthography , semantics , and syntactic distributions to induce morphological relationships in German , Dutch , and English . Using CELEX as a gold standard for evaluation , we show our algorithm to be an improvement over any knowledge-free algorithm yet proposed . Many NLP tasks , such as building machine-readable dictionaries , are dependent on the results of morphological analysis . While morphological analyzers have existed since the early 1960s , current algorithms require human labor to build rules for morphological structure . In an attempt to avoid this labor-intensive process , recent work has focused on machine-learning approaches to induce morphological structure using large corpora . In this paper , we propose a knowledge-free algorithm to automatically induce the morphology structures of a language . Our algorithm takes as input a large corpus and produces as output a set of conflation sets indicating the various inflected and derived forms for each word in the language . As an example , the conflation set of the word " abuse " would contain " abuse " , " abused " , " abuses " , " abusive " , " abusively " , and so forth . Our algorithm extends earlier approaches to morphology induction by combining various induced information sources : the semantic relatedness of the affixed forms using a Latent Semantic Analysis approach to corpusbased semantics ( Schone and Jurafsky , 2000 ) , affix frequency , syntactic context , and transitive closure . Using the hand-labeled CELEX lexicon ( Baayen , et al . , 1993 ) as our gold standard , the current version of our algorithm achieves an F-score of 88.1 % on the task of identifying conflation sets in English , outperforming earlier algorithms . Our algorithm is also applied to German and Dutch and evaluated on its ability to find prefixes , suffixes , and circumfixes in these languages . To our knowledge , this serves as the first evaluation of complete regular morphological induction of German or Dutch ( although researchers such as Nakisa and Hahn ( 1996 ) have evaluated induction algorithms on morphological sub-problems in German ) . We have illustrated three extensions to our earlier morphology induction work ( Schone and Jurafsky ( 2000 ) ) . In addition to induced semantics , we incorporated induced orthographic , syntactic , and transitive information resulting in almost a 20 % relative reduction in overall induction error . We have also extended the work by illustrating performance in German and Dutch where , to our knowledge , complete morphology induction performance measures have not previously been obtained . Lastly , we showed a mechanism whereby circumfixes as well as combinations of prefixing and suffixing can be induced in lieu of the suffixonly strategies prevailing in most previous research . For the future , we expect improvements could be derived by coupling this work , which focuses primarily on inducing regular morphology , with that of Yarowsky and Wicentowski ( 2000 ) , who assume some information about regular morphology in order to induce irregular morphology . We also believe that some findings of this work can benefit other areas of linguistic induction , such as part of speech .

Challenge: To avoid human labor to build rules, machine-leaning approaches have been studied to induce morphological structures using large corpora.
Approach: They propose a knowledge-free algorithm that induces the morphology of inflectional languages by combining cues from orthography, semantics, and syntactic distributions.
Outcome: Their method is applied to German, Dutch, and English, and improves over any knowledge-free algorithm using the CELEX lexicon as a gold standard in English.