On 4 October at 14:15 Eleri Aedmaa will defend her doctoral thesis „Detecting Compositionality of Estonian Particle Verbs with Statistical and Linguistic Methods“.
Associate Professor Kadri Muischnek
Dr Kristel Uiboaed.
PhD Carlos Ramisch (Aix Marseille Université).
Nowadays, applications that process human languages (including Estonian) are part of everyday life. However, computers are not yet able to understand every nuance of language. Machine translation is probably the most well-known application of natural language processing. Occasionally, the worst failures of machine translation systems (e.g. Google Translate) are shared on social media. Most of such cases happen when sequences longer than words are translated. For example, translation systems are not able to catch the correct meaning of the particle verb alt (‘from under’) minema (‘to go’) (‘to get deceived’) in the sentence Ta läks lepinguga alt because the literal translation of the components of the expression is not correct. In order to improve the quality of machine translation systems and other useful applications, e.g. spam detection or question answering systems, such (idiomatic) multi-word expressions and their meanings must be well detected. The detection of multi-word expressions and their meaning is important in all languages and therefore much research has been done in the field, especially in English. However, the suggested methods have not been applied to the detection of Estonian multi-word expressions before. The dissertation fills that gap and applies well-known machine learning methods to detect one type of Estonian multi-word expressions – the particle verbs. Based on large textual data, the thesis demonstrates that the traditional binary division of Estonian particle verbs to non-compositional (ainukordne, meaning is not predictable from the meaning of its components) and compositional (korrapärane, meaning is predictable from the meaning of its components) is not comprehensive enough. The research confirms the widely adopted view in computational linguistics that the multi-word expressions form a continuum between the compositional and non-compositional units. Moreover, it is shown that in addition to context, there are some linguistic features, e.g. the animacy and cases of subject and object that help computers to predict whether the meaning of a particle verb in a sentence is compositional or non-compositional. In addition, the research introduces novel resources for Estonian language – trained embeddings and created compositionality datasets are available for the future research.