Review of coreference resolution in English and Persian

Coreference resolution (CR) is one of the most challenging areas of natural language processing. This task seeks to identify all textual references to the same real-world entity. Research in this field is divided into coreference resolution and anaphora resolution. Due to its application in textual comprehension and its utility in other tasks such as information extraction systems, document summarization, and machine translation, this field has attracted considerable interest. Consequently, it has a significant effect on the quality of these systems. This article reviews the existing corpora and evaluation metrics in this field. Then, an overview of the coreference algorithms, from rule-based methods to the latest deep learning techniques, is provided. Finally, coreference resolution and pronoun resolution systems in Persian are investigated.Comment: 44 pages, 11 figures, 5 table

Coreference resolution: maximum metric score training, domain adaptation, and zero pronoun resolution

Ph.DDOCTOR OF PHILOSOPH

Towards Multilingual Coreference Resolution

The current work investigates the problems that occur when coreference resolution is considered as a multilingual task. We assess the issues that arise when a framework using the mention-pair coreference resolution model and memory-based learning for the resolution process are used. Along the way, we revise three essential subtasks of coreference resolution: mention detection, mention head detection and feature selection. For each of these aspects we propose various multilingual solutions including both heuristic, rule-based and machine learning methods. We carry out a detailed analysis that includes eight different languages (Arabic, Catalan, Chinese, Dutch, English, German, Italian and Spanish) for which datasets were provided by the only two multilingual shared tasks on coreference resolution held so far: SemEval-2 and CoNLL-2012. Our investigation shows that, although complex, the coreference resolution task can be targeted in a multilingual and even language independent way. We proposed machine learning methods for each of the subtasks that are affected by the transition, evaluated and compared them to the performance of rule-based and heuristic approaches. Our results confirmed that machine learning provides the needed flexibility for the multilingual task and that the minimal requirement for a language independent system is a part-of-speech annotation layer provided for each of the approached languages. We also showed that the performance of the system can be improved by introducing other layers of linguistic annotations, such as syntactic parses (in the form of either constituency or dependency parses), named entity information, predicate argument structure, etc. Additionally, we discuss the problems occurring in the proposed approaches and suggest possibilities for their improvement

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines

Character Extraction and Character Type Identification from Summarised Story Plots

[EN] Identifying the characters from free-form text and understanding the roles and relationships between them is an evolving area of research. They have a wide range of applications, from summarising narrations to understanding the social network from social media tweets, which can help in automation and improve the experience of AI systems like chatbots and much more. The aim of this research is twofold. Firstly, we aim to develop an effective method of extracting characters from a story summary, to develop a set of relevant features, then, using supervised learning algorithms, to identify the character types. Secondly, we aim to examine the efficacy of unsupervised learning algorithms in type identification, as it is challenging to find a dataset with a predetermined list of characters, roles, and relationships that are essential for supervised learning. To do so, we used summary plots of fictional stories to experiment and evaluate our approach. Our character extraction approach successfully improved on the performance reported by existing work, with an average F1-score of 0.86. Supervised learning algorithms successfully identified the character types and achieved an overall average F1-score of 0.94. However, the clustering algorithms identified more than three clusters, indicating that more research is needed to improve their efficacy.Srinivasan, V.; Power, A. (2022). Character Extraction and Character Type Identification from Summarised Story Plots. Journal of Computer-Assisted Linguistic Research. 6:19-41. https://doi.org/10.4995/jclr.2022.178351941