Diana Trandabat

Also published as: Diana Marie Trandabăţ, Diana Trandabăţ, Diana Trandabăț


2021

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FII_CROSS at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation
Ciprian Bodnar | Andrada Tapuc | Cosmin Pintilie | Daniela Gifu | Diana Trandabat
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents a word-in-context disambiguation system. The task focuses on capturing the polysemous nature of words in a multilingual and cross-lingual setting, without considering a strict inventory of word meanings. The system applies Natural Language Processing algorithms on datasets from SemEval 2021 Task 2, being able to identify the meaning of words for the languages Arabic, Chinese, English, French and Russian, without making use of any additional mono- or multilingual resources.

2020

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CoBiLiRo: A Research Platform for Bimodal Corpora
Dan Cristea | Ionuț Pistol | Șerban Boghiu | Anca-Diana Bibiri | Daniela Gîfu | Andrei Scutelnicu | Mihaela Onofrei | Diana Trandabăț | George Bugeag
Proceedings of the 1st International Workshop on Language Technology Platforms

This paper describes the on-going work carried out within the CoBiLiRo (Bimodal Corpus for Romanian Language) research project, part of ReTeRom (Resources and Technologies for Developing Human-Machine Interfaces in Romanian). Data annotation finds increasing use in speech recognition and synthesis with the goal to support learning processes. In this context, a variety of different annotation systems for application to Speech and Text Processing environments have been presented. Even if many designs for the data annotations workflow have emerged, the process of handling metadata, to manage complex user-defined annotations, is not covered enough. We propose a design of the format aimed to serve as an annotation standard for bimodal resources, which facilitates searching, editing and statistical analysis operations over it. The design and implementation of an infrastructure that houses the resources are also presented. The goal is widening the dissemination of bimodal corpora for research valorisation and use in applications. Also, this study reports on the main operations of the web Platform which hosts the corpus and the automatic conversion flows that brings the submitted files at the format accepted by the Platform.

2019

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Hope at SemEval-2019 Task 6: Mining social media language to discover offensive language
Gabriel Florentin Patras | Diana Florina Lungu | Daniela Gifu | Diana Trandabat
Proceedings of the 13th International Workshop on Semantic Evaluation

User’s content share through social media has reached huge proportions nowadays. However, along with the free expression of thoughts on social media, people risk getting exposed to various aggressive statements. In this paper, we present a system able to identify and classify offensive user-generated content.

2018

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EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #Tweets
Ramona-Andreea Turcu | Sandra Maria Amarandei | Iuliana-Alexandra Flescan-Lovin-Arseni | Daniela Gifu | Diana Trandabat
Proceedings of the 12th International Workshop on Semantic Evaluation

The „Affect in Tweets” task is centered on emotions categorization and evaluation matrix using multi-language tweets (English and Spanish). In this research, SemEval Affect dataset was preprocessed, categorized, and evaluated accordingly (precision, recall, and accuracy). The system described in this paper is based on the implementation of supervised machine learning (Naive Bayes, KNN and SVM), deep learning (NN Tensor Flow model), and decision trees algorithms.

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The Dabblers at SemEval-2018 Task 2: Multilingual Emoji Prediction
Larisa Alexa | Alina Lorenț | Daniela Gîfu | Diana Trandabăț
Proceedings of the 12th International Workshop on Semantic Evaluation

The “Multilingual Emoji Prediction” task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In order to do that, we used supervised machine learning (Naive Bayes) and deep learning (Recursive Neural Network).

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Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies
Mihaela Onofrei | Ionuț Hulub | Diana Trandabăț | Daniela Gîfu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper presents the participation of Apollo’s team in the SemEval-2018 Task 9 “Hypernym Discovery”, Subtask 1: “General-Purpose Hypernym Discovery”, which tries to produce a ranked list of hypernyms for a specific term. We propose a novel approach for automatic extraction of hypernymy relations from a corpus by using dependency patterns. We estimated that the application of these patterns leads to a higher score than using the traditional lexical patterns.

2017

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Wild Devs’ at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity
Răzvan-Gabriel Rotari | Ionuț Hulub | Ștefan Oprea | Mihaela Plămadă-Onofrei | Alina Beatrice Lorenţ | Raluca Preisler | Adrian Iftene | Diana Trandabăț
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper presents Wild Devs’ participation in the SemEval-2017 Task 2 “Multi-lingual and Cross-lingual Semantic Word Similarity”, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the output consists of a list of scores, from 0 to 4, corresponding to the degree of similarity between the word pairs.

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#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets
Iuliana Alexandra Fleșcan-Lovin-Arseni | Ramona Andreea Turcu | Cristina Sîrbu | Larisa Alexa | Sandra Maria Amarandei | Nichita Herciu | Constantin Scutaru | Diana Trandabăț | Adrian Iftene
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper presents the participation of #WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. #WarTeam’s system uses a neural network (TensorFlow) having inputs from a Naïve Bayes humor classifier and a sentiment analyzer.

2016

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SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard
Cosmin Florean | Oana Bejenaru | Eduard Apostol | Octavian Ciobanu | Adrian Iftene | Diana Trandabăţ
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?
Călin-Cristian Ciubotariu | Marius-Valentin Hrişca | Mihail Gliga | Diana Darabană | Diana Trandabăţ | Adrian Iftene
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2011

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Sentimatrix – Multilingual Sentiment Analysis Service
Alexandru-Lucian Gînscă | Emanuela Boroș | Adrian Iftene | Diana Trandabăț | Mihai Toader | Marius Corîci | Cenel-Augusto Perez | Dan Cristea
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)

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Using semantic roles to improve summaries
Diana Trandabăț
Proceedings of the 13th European Workshop on Natural Language Generation

2010

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An Optimal and Portable Parsing Method for Romanian, French, and German Large Dictionaries
Neculai Curteanu | Alex Moruz | Diana Trandabăţ
Proceedings of the 2nd Workshop on Cognitive Aspects of the Lexicon

2008

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Extracting Sense Trees from the Romanian Thesaurus by Sense Segmentation & Dependency Parsing
Neculai Curteanu | Alex Moruz | Diana Trandabăţ
Coling 2008: Proceedings of the Workshop on Cognitive Aspects of the Lexicon (COGALEX 2008)

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Romanian Semantic Role Resource
Diana Trandabăţ | Maria Husarciuc
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Semantic databases are a stable starting point in developing knowledge based systems. Since creating language resources demands many temporal, financial and human resources, a possible solution could be the import of a resource annotation from one language to another. This paper presents the creation of a semantic role database for Romanian, starting from the English FrameNet semantic resource. The intuition behind the importing program is that most of the frames defined in the English FN are likely to be valid cross-lingual, since semantic frames express conceptual structures, language independent at the deep structure level. The surface realization, the surface level, is realized according to each language syntactic constraints. In the paper we present the advantages of choosing to import the English FrameNet annotation, instead of annotating a new corpus. We also take into account the mismatches encountered in the validation process. The rules created to manage particular situations are used to improve the import program. We believe the information and argumentations in this paper could be of interest for those who wish develop FrameNet-like systems for other languages.

2007

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Semantic Frames in Romanian Natural Language Processing Systems
Diana Marie Trandabăţ
Proceedings of the NAACL-HLT 2007 Doctoral Consortium