Automatic Prediction of Morphosemantic Relations (2016)

Abstract: This paper presents a machine learning method for automatic identification and classification of morphosemantic relations (MSRs) between verb and noun synset pairs in the Bulgarian WordNet (BulNet). The core training data comprise 6,641 morphosemantically related verb–noun literal pairs from BulNet. The core dataset were preprocessed quality-wise by applying validation and reorganisation procedures. Further, the data were supplemented with negative examples of literal pairs not linked by an MSR. The designed supervised machine learning method uses the RandomTree algorithm and is implemented in Java with the Weka package. A set of experiments were performed to test various approaches to the task. Future work on improving the classifier includes adding more training data, employing more features, and fine-tuning. Apart from the language specific information about derivational processes, the proposed method is language independent.

## introduction this paper investigates a machine learning method for identification and classification of morphosemantic relations (msrs) between verb and noun synset pairs in the bulgarian wordnet (bulnet). it is based on the msr dataset from the princeton wordnet (pwn) @xcite , automatically imported to the bulgarian wordnet (the core dataset), the pwn semantic primitives (henceforth, semantic primes) and the derivational relations (drs) in the bulgarian wordnet assigned automatically using a string similarity algorithm combined with heuristics @xcite , followed by manual post-editing. the msrs link verb-noun pairs of synsets that contain derivationally related literals. as semantic and morphosemantic relations refer to concepts, they are universal, and such a relation must hold between the relevant concepts in any language, regardless of whether it is morphologically expressed or not. this has enabled the automatic transfer of the relations to other languages, such as polish @xcite , bulgarian @xcite @xcite , serbian @xcite , @xcite . other sets of msrs have been proposed for turkish @xcite , czech @xcite , estonian @xcite , polish @xcite , croatian @xcite . the study is motivated by the fact that a considerable number (53.2%, or 16,914) of the verbnoun synsets exhibiting a derivational relation @xcite in the pwn 3.0 is not labelled with an msr. in addition, the linguistic generalisations behind the existing msrs have been made on the basis of english derivational morphology, hence the proposed set of msr instances may be extended based on evidence from the derivational morphology of other languages, including bulgarian. the present research builds on @xcite , where plausible msrs were assigned by intersecting the following pairs registered in bul-@xmath0. then the probability for each msr was estimated given the frequency of occurrence of the @xmath1, and was used to filter out less probable msrs. in a follow-up paper @xcite , a decision-tree based supervised machine learning method for classification of msrs was designed, implemented and tested. in the present paper, we upgrade the previous research along the following lines -we propose a method designed to identify new synset pairs that have a high probability of being msr related and to classify the respective msrs; we test new sets of features combined in different ways (as described in the experiments), which gives us insights into possible extensions and improvements of the method. our task is three-fold: (i) to find out potential derivational verb-noun pairs in bulnet; (ii) for a given potential derivational pair, the classifier must determine whether a derivational relation exists (or there is just a formal coincidence); (iii) if a dr exists, decide what type of msr connects the relevant synsets. the first part of the task was implemented by identifying common substrings shared by nounverb literal pairs and by mapping the resulting endings to the canonical suffixes. the implementation of (ii) and (iii) was performed using a machine learning classifier. the suffixes of the noun-verb derivational pairs and the semantic primes of the verb and noun synsets were used as features in the learning, while the types of msr between these pairs of synsets were the classes in the classification task. our research is focused on bulgarian but the results are transferable across languages and the methodology can be used to enhance wordnets for other languages with semantic content. ## linguistic preprocessing of training data we performed the following consistency procedures on the wordnet structure: (i) manual inspection and validation of msrs in case of multiple relations assigned to a synset pair; (ii) validation of the consistency of the semantic primes in the hypernym-hyponyms paths; (iii) consistency check of the type of the assigned msr against the semantic primes. the quality analysis and validation is performed only on the core dataset and is language independent, i.e., it concerns the wordnet structure, rather than any language data, and is transferrable across wordnets. this is a oneoff task, ensuring the quality of the data used for machine learning, as well as for any future tasks based on these data. ## conclusion and future work our future work will be focused on the enhancement of the method by exploring at least two mutually related directions: (i) automatic harvesting of more labelled data from other wordnets; (ii) incorporation of new features for classification and assignment of relations including heuristics derived from the wordnet structure. alongside the introduction of new features, it is necessary to develop techniques for reducing redundant features, as well as for correlation-based feature selection, feature ranking or principal component analysis. we are planning to devise experiments on extended datasets with more data for english and romanian. the multilingual data can contribute to the training with respect to the possible pairs of verb-noun primes and the relevant semantic re-strictions. while part of the information employed in this paper, such as the suffix lists and mappings from word endings to canonical suffixes, is language specific, the method proposed is language independent, including the linguistic processing of the data. testing it for other languages is a task we envisage to implement in the future.