Shallow Morphological Analysis in Monolingual Information Retrieval for Dutch, German, and Italian
CLEF '01 Revised Papers from the Second Workshop of the Cross-Language Evaluation Forum on Evaluation of Cross-Language Information Retrieval Systems
Empirical methods for compound splitting
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Stemming and decompounding for German text retrieval
ECIR'03 Proceedings of the 25th European conference on IR research
Social Semantics and Its Evaluation by Means of Semantic Relatedness and Open Topic Models
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Data-driven compound splitting method for english compounds in domain names
Proceedings of the 18th ACM conference on Information and knowledge management
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Algorithms for the verification of the semantic relation between a compound and a given lexeme
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Generation of compound words in statistical machine translation into compounding languages
Computational Linguistics
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We present an approach for knowledge-free and unsupervised recognition of compound nouns for languages that use one-wordcompounds such as Germanic and Scandinavian languages. Our approach works by creating a candidate list of compound splits based on the word list of a large corpus. Then, we filter this list using the following criteria: (a) frequencies of compounds and parts, (b) length of parts. In a second step, we search the corpus for periphrases, that is a reformulation of the (single-word) compound using the parts and very high frequency words (which are usually prepositions or determiners). This step excludes spurious candidate splits at cost of recall. To increase recall again, we train a trie-based classifier that also allows splitting multipart-compounds iteratively. We evaluate our method for both steps and with various parameter settings for German against a manually created gold standard, showing promising results above 80% precision for the splits and about half of the compounds periphrased correctly. Our method is language independent to a large extent, since we use neither knowledge about the language nor other language-dependent preprocessing tools. For compounding languages, this method can drastically alleviate the lexicon acquisition bottleneck, since even rare or yet unseen compounds can now be periphrased: the analysis then only needs to have the parts described in the lexicon, not the compound itself.