Towards the extraction of cross-sentence relations through event extraction and entity coreference

Cross-sentence relation extraction deals with the extraction of relations beyond the sentence boundary. This thesis focuses on two of the NLP tasks which are of importance to the successful extraction of cross-sentence relation mentions: event extraction and coreference resolution. The first part of the thesis focuses on addressing data sparsity issues in event extraction. We propose a self-training approach for obtaining additional labeled examples for the task. The process starts off with a Bi-LSTM event tagger trained on a small labeled data set which is used to discover new event instances in a large collection of unstructured text. The high confidence model predictions are selected to construct a data set of automatically-labeled training examples. We present several ways in which the resulting data set can be used for re-training the event tagger in conjunction with the initial labeled data. The best configuration achieves statistically significant improvement over the baseline on the ACE 2005 test set (macro-F1), as well as in a 10-fold cross validation (micro- and macro-F1) evaluation. Our error analysis reveals that the augmentation approach is especially beneficial for the classification of the most under-represented event types in the original data set. The second part of the thesis focuses on the problem of coreference resolution. While a certain level of precision can be reached by modeling surface information about entity mentions, their successful resolution often depends on semantic or world knowledge. This thesis investigates an unsupervised source of such knowledge, namely distributed word representations. We present several ways in which word embeddings can be utilized to extract features for a supervised coreference resolver. Our evaluation results and error analysis show that each of these features helps improve over the baseline coreference system’s performance, with a statistically significant improvement (CoNLL F1) achieved when the proposed features are used jointly. Moreover, all features lead to a reduction in the amount of precision errors in resolving references between common nouns, demonstrating that they successfully incorporate semantic information into the process

MT Summit workshop proceedings for: Multi-word Units in Machine Translation and Translation Technologies (Organised at the 14th Machine Translation Summit)

Machine Translation (MT) has evolved along with different types of computer-assisted translation tools and a notable progress has been achieved in improving the quality of translations. However, in spite of the recent positive developments in translation technologies, not all problems have been solved and in particular the identification, interpretation and translation of multi-word units (MWUs) still represent open challenges, both from a theoretical and a practical point of view. The low standard of analysis and translation of MWUs in translation technologies suggest that there is the need to invest in further research with the goal of improving the performance of the various translation applications. Multi-word units (MWUs) are a complex linguistic phenomenon, ranging from lexical units with a relatively high degree of internal variability to expressions that are frozen or semi-frozen. Such units are very frequent both in everyday language and in languages for special purposes. Their interpretation and translation sometimes present unexpected obstacles even to human translators, mainly because of intrinsic ambiguities, structural and lexical asymmetries between languages, and, finally, cultural differences. The current theoretical work on this topic deals with different formalisms and techniques relevant for MWU processing in MT as well as other translation applications, such as: automatic recognition of MWUs in a monolingual or bilingual setting; alignment and paraphrasing methodologies; development, features and usefulness of handcrafted monolingual and bilingual linguistic resources and grammars; use of MWUs in Statistical Machine Translation (SMT) domain adaptation, as well as empirical work concerning their modelling accuracy and descriptive adequacy across various language pairs. At the practical level, the issue of MWU has been addressed in various MT approaches, whether knowledge-based, statistical (word-based, phrase-based or factored-based) or hybrid. In general, MWU identification and translation problems are far from being solved and there is still considerable room for improvement. There is a recent growing attention to MWU processing in MT and Translation Technologies, as it has been acknowledged that it is not possible to create large-scale applications without properly handling MWUs of all kinds. The focus of this workshop is to address the MWU issue in a synergetic way, taking advantage of the recent developments in disciplines such as Linguistics, Translation Studies, Computational Linguistics, and Computational Phraseology. The main aim of the Workshop is, therefore, to bring together researchers working on various aspects of MWU processing in different disciplines, in order to discuss and propose innovative ideas and methods in relation to MT and Translation Technologies. In particular, this workshop welcomes the exchange of interactions between researchers in NLP working on the computational treatment of multi-word units, experts in phraseology (including computational phraseology) working on challenging topics of their discipline, as well as translation practitioners, to the benefit of applying their latest results to advance the state of the art in MWU translation