Adrian Iftene


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UAICS at SemEval-2020 Task 4: Using a Bidirectional Transformer for Task a
Ciprian-Gabriel Cusmuliuc | Lucia-Georgiana Coca | Adrian Iftene
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Commonsense Validation and Explanation has been a difficult task for machines since the dawn of computing. Although very trivial to humans it poses a high complexity for machines due to the necessity of inference over a pre-existing knowledge base. In order to try and solve this problem the SemEval 2020 Task 4 - ”Commonsense Validation and Explanation (ComVE)” aims to evaluate systems capable of multiple stages of ComVE. The challenge includes 3 tasks (A, B and C), each with it’s own requirements. Our team participated only in task A which required selecting the statement that made the least sense. We choose to use a bidirectional transformer in order to solve the challenge, this paper presents the details of our method, runs and result.

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A Real-Time System for Credibility on Twitter
Adrian Iftene | Daniela Gifu | Andrei-Remus Miron | Mihai-Stefan Dudu
Proceedings of the 12th Language Resources and Evaluation Conference

Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.


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UAIC at SemEval-2019 Task 3: Extracting Much from Little
Cristian Simionescu | Ingrid Stoleru | Diana Lucaci | Gheorghe Balan | Iulian Bute | Adrian Iftene
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we present a system description for implementing a sentiment analysis agent capable of interpreting the state of an interlocutor engaged in short three message conversations. We present the results and observations of our work and which parts could be further improved in the future.


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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.


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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)


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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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Named Entity Relation Mining using Wikipedia
Adrian Iftene | Alexandra Balahur-Dobrescu
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Discovering relations among Named Entities (NEs) from large corpora is both a challenging, as well as useful task in the domain of Natural Language Processing, with applications in Information Retrieval (IR), Summarization (SUM), Question Answering (QA) and Textual Entailment (TE). The work we present resulted from the attempt to solve practical issues we were confronted with while building systems for the tasks of Textual Entailment Recognition and Question Answering, respectively. The approach consists in applying grammar induced extraction patterns on a large corpus - Wikipedia - for the extraction of relations between a given Named Entity and other Named Entities. The results obtained are high in precision, determining a reliable and useful application of the built resource.


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Hypothesis Transformation and Semantic Variability Rules Used in Recognizing Textual Entailment
Adrian Iftene | Alexandra Balahur-Dobrescu
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing