F
Ferran Pla
Researcher at Polytechnic University of Valencia
Publications - 59
Citations - 875
Ferran Pla is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: SemEval & Sentiment analysis. The author has an hindex of 16, co-authored 58 publications receiving 770 citations.
Papers
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Journal Article
Shallow parsing using specialized hmms
Antonio Molina,Ferran Pla +1 more
TL;DR: A unified technique to solve different shallow parsing tasks as a tagging problem using a Hidden Markov Model-based approach (HMM), which constructs a Specialized HMM which gives more complete contextual models.
Proceedings Article
Political Tendency Identification in Twitter using Sentiment Analysis Techniques
Ferran Pla,Lluís F. Hurtado +1 more
TL;DR: The results obtained for the sentiment analysis task and the political tendency identification task are the best results published until now using the general corpus developed at TASS2013 workshop for Spanish.
Proceedings ArticleDOI
Tagging and chunking with bigrams
TL;DR: An integrated system for tagging and chunking texts from a certain language based on stochastic finite-state models that are learnt automatically, which is a very flexible and portable system.
Journal ArticleDOI
Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter
TL;DR: A model for irony detection based on the contextualization of pre-trained Twitter word embeddings by means of the Transformer architecture, which was the first ranked system in the Spanish corpus and has achieved the second-best result on the English corpus.
Journal ArticleDOI
Improving part-of-speech tagging using lexicalized HMMs
Ferran Pla,Antonio Molina +1 more
TL;DR: This work introduces a simple method to build Lexicalized Hidden Markov Models (L-HMMs) for improving the precision of part-of-speech tagging and conducts an exhaustive experimental comparison that shows that Lexicalization HMMs yield results which are better than or similar to other state- of-the-art part-Of- speech tagging approaches.