Author Archives: riilp

Paper accepted at NAACL 2019

Congratulations to Marcos Zampieri, whose paper has been accepted at NAACL 2019.

Reference:  Marcos  Zampieri,  Shervin  Malmasi,  Preslav  Nakov, Sara  Rosenthal,  Noura  Farra,  and  Ritesh  Kumar (2019)  Predicting the Type and Target of Offensive Posts in Social Media. 

You may access the NAACL paper here: https://arxiv.org/abs/1902.09666

Paper accepted at NAACL 2019

We are pleased to announce that the paper titled “Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions” from researchers in RGCL has been accepted into the main track of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019). This is joint work by Omid Rohanian, Shiva Taslimipoor, Le An Ha, Samaneh Kouchaki, and Prof. Ruslan Mitkov.

A preprint of this paper will soon be available on ArXiv.

Sheila Castilho and Natalia Resende spend a week at RGCL

This week we have had the pleasure of welcoming Dr Sheila Castilho and Dr Natalia Resende for a one week research stay at the Research Group in Computational Linguistics. Sheila and Natalia both come from the ADAPT Centre, Dublin and have come to discuss collaborations with members of our research group. During their stay, both Natalia and Sheila gave the group a talk about their research. The details of which can be found below:- 

 

Speaker: Dr Sheila Castilho

Date of talk: 19th November 2018

Title: Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation

Abstract: We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.

Speaker: Dr Natalia Resende

Date of talk: 21st November 2018

Title: Classifying nouns in Portuguese into gender categories: a deep learning approach

Abstract: In Portuguese, all nouns are distributed into two gender categories: feminine and masculine. On one hand, gender can be predicted from the phonological cues present in the endings of the nouns. For example, nouns ending in -a  tend to be feminine and nouns ending in -o   tend to be masculine. On the other hand, the relationship between word ending and gender is far from being a consistent rule, since nouns ending in other phonemes may be of either gender. In the present study, a connectionist network was trained to classify Portuguese nouns into gender categories considering their phonological structure as whole. The performance of the network was analysed in detail to check whether the network considers only the endings of the nouns or their whole phonological structure for gender decisions. In addition, it was analysed what type of information the network takes into account to decide the gender of nouns whose endings are not predictive of gender. Results show an error-free performance when the network takes into account the phonological information present in the endings of the nouns and frequency effects for nonpredictive endings. The present study has implications to the training of NLP systems when classifying nouns into gender categories.

Alexander Gelbukh gives a talk to RGCL

In August, Professor Alexander Gelbukh began a 12 month sabbatical at RGCL. As part of his visit, Prof. Gelbukh presented a research seminar to the group on ‘Opinion Mining and Sentiment Analysis’. During his time here, he has held many meetings with members of the group to discuss both future opportunities for collaboration, and discuss his research with interested people. 

Mireille Makary completes her Viva!

Congratulations to Mireille Makary for completing her Viva Voce exam on 17th October. Mireille, a part-time distance RGCL student, was defending her thesis ‘Ranking retrieval systems using minimal human assessments’.

After the Viva, Mireille celebrated with the group in the traditional RGCL way!

Research Seminar – Antonio Pascucci

Last week, Antonio Pascucci a visiting Ph.D. Industrial Student in Computational Linguistics for Authorship and Gender Attribution in Italian social media texts from Universiy Of Naples – L’Orientale, gave a Researcher Seminar to the group.

Title: ‘Computational Stylometry for Authorship Attribution in social media texts’

Abstract: 

Computational Stylometry (CS) is the study of stylistic features (linguistic choices). Writing style is a combination of decisions in language production. Thanks to a statistic analysis of these decisions, we can know author identity and many more characteristics about him/her. Writing style, in fact, is unique to an individual, and that’s why we talk about authorial DNA.

CS for the authorship attribution is the topic of my research project, and the aim is using CS for authorship attribution in social media texts. During the seminar, research project and my first steps in gender attribution will be shown, in addition to Cyberbullying detection researches, conducted thanks to a software made available by Expert System Corp.