COREFERENCE RESOLUTION IN INDONESIAN LANGUAGE USING WORD LEVEL COREFERENCE RESOLUTION ARHITECTURE - Muslim, Fajar
Coreference resolution is a problem in the field of text processing to find all mentions that refer to the same entity in the real world. Coreference resolution can be used to help solve problems in other natural language processing, namely entity linking, machine translation, summarization, chatbots, and question answering. Research on coreference resolution (coref) for Indonesian is still minimal. The coref research in Indonesian is relatively incomparable with each other because the data used are relatively different. The problems found in the Indonesian coref are problems with the dataset and problems with the algorithm. The problem with the dataset is that there is no standard dataset that can be used as a benchmark. The problem with the algorithm is that there is no research that uses the latest methods from several deep learning architectures that achieve competitive performance as in the English dataset. Another algorithm problem is that the best previous research still uses the pipelined system approach. This thesis is part of a joint research conducted by ITB, Prosa.ai, and AI Singapore. The research includes the creation of a Coreference Resolution in the Indonesian Language (COIN) dataset with standards that are adapted to the OntoNotes dataset standards and modeling using the c2f-coref and wl-coref architectures. This thesis has scope to build program code and carry out experiments using word level coreference resolution (wl-coref) architecture. In addition, there is an architectural experiment of Higher-order Coreference Resolution with Coarse-to-fine Inference (c2f-coref) with a variation of the BERT encoder carried out by engineers from AI Singapore. The analysis is carried out together to compare and analyze the performance of the model. The wl-coref architecture was chosen as the solution in this thesis due to its efficiency and competitive performance. The step in the wl-coref architecture is to find coreference links between word tokens, then perform span construction from