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The Comparative Evaluation of Dependency Parsers in Parsing Estonian Master
| Content Provider | Semantic Scholar |
|---|---|
| Author | Alam, Nusaeb Nur |
| Copyright Year | 2017 |
| Abstract | Natural Language Processing (NLP) technology has been constantly developing and has seen a vast improvement in the last couple of decades. One key task in NLP is dependency parsing that oftentimes is a pre-requisite for many other tasks such as machine translation, Named Entity Recognition (NER) and so on. The idea of dependency parsing is to perform a syntactic analysis of a sentence and extract the grammatical relations among the words in that sentence. Most research on dependency parsing has been focusing on English text parsing. In this thesis, an effort has been made to evaluate and compare the performance of some of the state-of-the-art dependency parsers in parsing Estonian. The dependency parsers chosen for evaluation are: MaltParser, spaCy, Stanford neural network dependency parser (nndep), SyntaxNet and UDPipe. The comparison is done using mainly Labelled Attachment Score (LAS), Unlabelled Attachment Score (UAS) and Label Accuracy (LA). New models for Estonian were trained for the spaCy, Stanford nndep and UDPipe parsers while pre-trained models for the MaltParser and SyntaxNet were used in the experiments. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://comserv.cs.ut.ee/home/files/Alam_SoftwareEngineering_2017.pdf?reference=5CB15D24E8BA09920EF4BFDA562AEEC749468FD9&study=ATILoputoo |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
