TrainingSet

We report here on our experiments with POST (Part of Speech Tagger) to address problems of ambiguity and of understanding unknown words. Part of speech tagging, per se, is a …...

This paper describes the status of the MIT ATIS system as of February 1992, focusing especially on the changes made to the SUMMIT recognizer. These include context-dependent phonetic modelling, the …...

This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled …...

In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. A maximum entropy tagger is trained on manually annotated data to automatically assign to Chinese …...

We determine the subjectivity of word senses. To avoid costly annotation, we evaluate how useful existing resources es- tablished in opinion mining are for this task. We show that results …...

This paper presents the description and evaluation framework of SemEval-2010 Word Sense Induction & Disambiguation task, as well as the evaluation results of 26 participating systems. In this task, partici- …...

This paper describes the SHELLFBK system that participated in SemEval 2015 Tasks 9, 10, and 11. Our system takes a supervised approach that builds on techniques from in- formation retrieval. …...

This paper presents the participation of the TALN team in the Complex Word Identifica- tion Task of SemEval-2016 (Task 11). The purpose of the task was to determine if a …...

Extracting information from semi- structured text has been studied only for limited domain sources due to its hetero- geneous formats. This paper proposes a Ripple-Down Rules (RDR) based ap- proach …...

Complement phrases are essential for constructing well-formed sentences in German. Identifying verb complements and categorizing complement classes is challenging even for linguists who are specialized in the field of verb …...

AbstractThis paper describes the Irony detection system that participates in SemEval-2018 Task 3: Irony detection in English tweets. The system participated in the subtasks A and B. This paper discusses …...

AbstractThis paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were …...

AbstractRecent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer precomputed embeddings from popular contextual ELMo model …...

AbstractThis position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of …...

AbstractThe paper describes a transformer-based system designed for SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. The purpose of the task was to predict the intimacy of tweets in a range …...