This paper describes in details the first version of Morphonette, a new French morphological resource and a new radically lexeme-based method of morphological analysis. This research is grounded in a paradigmatic conception of derivational morphology where the morphological structure is a structure of the entire lexicon and not one of the individual words it contains. The discovery of this structure relies on a measure of morphological similarity between words, on formal analogy and on the properties of two morphological paradigms:
The Morphonette network could also be compared to the morphological families constructed by @cite_15 , @cite_5 or @cite_8 among others. It is also very close to Polymots, a manually-constructed morphological lexicon . Polymots and Morphonette are complementary since the former primarily contains short words while the latter mainly contains long words because of the criteria we have used to select the morphological relations.
@cite_5 @cite_15 @cite_8
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Electronic dictionaries offer many possibilities unavailable in paper dictionaries to view, display or access information. However, even these resources fall short when it comes to access words sharing semantic features and certain aspects of form: few applications offer the possibility to access a word via a morphologically or semantically related word. In this paper, we present such an application, POLYMOTS, a lexical database for contemporary French containing 20.000 words grouped in 2.000 families. The purpose of this resource is to group words into families on the basis of shared morpho-phonological and semantic information. Words with a common stem form a family; words in a family also share a set of common conceptual fragments (in some families there is a continuity of meaning, in others meaning is distributed). With this approach, we capitalize on the bidirectional link between semantics and morpho-phonology : the user can thus access words not only on the basis of ideas, but also on the basis of formal characteristics of the word, i.e. its morphological features. The resulting lexical database should help people learn French vocabulary and assist them to find words they are looking for, going thus beyond other existing lexical resources. The paper presents a computational model aiming at making the morphological structure of the lexicon emerge from the formal and semantic regularities of the words it contains. The model is purely lexeme-based. The proposed morphological structure consists of (1) binary relations that connect each headword with words that are morphologically related, and especially with the members of its morphological family and its derivational series, and of (2) the analogies that hold between the words. The model has been tested on the lexicon of French using the TLFi machine readable dictionary. This paper investigates a novel approach to unsupervised morphology induction relying on community detection in networks. In a first step, morphological transformation rules are automatically acquired based on graphical similarities between words. These rules encode substring substitutions for transforming one word form into another. The transformation rules are then applied to the construction of a lexical network. The nodes of the network stand for words while edges represent transformation rules. In the next step, a clustering algorithm is applied to the network to detect families of morphologically related words. Finally, morpheme analyses are produced based on the transformation rules and the word families obtained after clustering. While still in its preliminary development stages, this method obtained encouraging results at Morpho Challenge 2009, which demonstrate the viability of the approach. This paper compares automatically generated sets of synonyms in French and Slovene wordnets with respect to the resources used in the construction process. Polysemous words were disambiguated via a five-language word-alignment of the SEERA.NET parallel corpus, a subcorpus of the JRC Acquis. The extracted multilingual lexicon was disambiguated with the existing wordnets for these languages. On the other hand, a bilingual approach sufficed to acquire equivalents for monosemous words. Bilingual lexicons were extracted from different resources, including Wikipedia, Wiktionary and EUROVOC thesaurus. A representative sample of the generated synsets was evaluated against the goldstandards.