Iliana Simova


2017

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Word Embeddings as Features for Supervised Coreference Resolution
Iliana Simova | Hans Uszkoreit
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

A common reason for errors in coreference resolution is the lack of semantic information to help determine the compatibility between mentions referring to the same entity. Distributed representations, which have been shown successful in encoding relatedness between words, could potentially be a good source of such knowledge. Moreover, being obtained in an unsupervised manner, they could help address data sparsity issues in labeled training data at a small cost. In this work we investigate whether and to what extend features derived from word embeddings can be successfully used for supervised coreference resolution. We experiment with several word embedding models, and several different types of embeddingbased features, including embedding cluster and cosine similarity-based features. Our evaluations show improvements in the performance of a supervised state-of-theart coreference system.

2015

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Factored models for Deep Machine Translation
Kiril Simov | Iliana Simova | Velislava Todorova | Petya Osenova
Proceedings of the 1st Deep Machine Translation Workshop

2014

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Joint Ensemble Model for POS Tagging and Dependency Parsing
Iliana Simova | Dimitar Vasilev | Alexander Popov | Kiril Simov | Petya Osenova
Proceedings of the First Joint Workshop on Statistical Parsing of Morphologically Rich Languages and Syntactic Analysis of Non-Canonical Languages

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A System for Experiments with Dependency Parsers
Kiril Simov | Iliana Simova | Ginka Ivanova | Maria Mateva | Petya Osenova
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present a system for experimenting with combinations of dependency parsers. The system supports initial training of different parsing models, creation of parsebank(s) with these models, and different strategies for the construction of ensemble models aimed at improving the output of the individual models by voting. The system employs two algorithms for construction of dependency trees from several parses of the same sentence and several ways for ranking of the arcs in the resulting trees. We have performed experiments with state-of-the-art dependency parsers including MaltParser, MSTParser, TurboParser, and MATEParser, on the data from the Bulgarian treebank -- BulTreeBank. Our best result from these experiments is slightly better then the best result reported in the literature for this language.

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Multiword Expressions in Machine Translation
Valia Kordoni | Iliana Simova
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This work describes an experimental evaluation of the significance of phrasal verb treatment for obtaining better quality statistical machine translation (SMT) results. The importance of the detection and special treatment of phrasal verbs is measured in the context of SMT, where the word-for-word translation of these units often produces incoherent results. Two ways of integrating phrasal verb information in a phrase-based SMT system are presented. Automatic and manual evaluations of the results reveal improvements in the translation quality in both experiments.