Articles

AbstractBuilding large datasets annotated with semantic information, such as FrameNet, is an expensive process. Consequently, such resources are unavailable for many languages and specific domains. This problem can be alleviated by using unsupervised approaches to induce the frames evoked by a collection of documents. That is the objective of the …...

AbstractWe present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (Qasem-iZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering …...

AbstractWe present an encoder-decoder model for semantic parsing with UCCA SemEval 2019 Task 1. The encoder is a Bi-LSTM and the decoder uses recursive self-attention. The proposed model alleviates challenges and feature engineering in traditional transition-based and graph-based parsers. The resulting parser is simple and proved to effective on the …...

AbstractOur submission for Task 1 ‘Cross-lingual Semantic Parsing with UCCA’ at SemEval-2018 is a feed-forward neural network that builds upon an existing state-of-the-art transition-based directed acyclic graph parser. We replace most of its features by deep contextualized word embeddings and introduce an approximation to represent non-terminal nodes in the graph …...

AbstractThis paper describes our recursive system for SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA. Each recursive step consists of two parts. We first perform semantic parsing using a sequence tagger to estimate the probabilities of the UCCA categories in the sentence. Then, we apply a decoding policy which interprets …...

AbstractThis paper describes the system submitted to the SemEval 2019 shared task 1 ‘Cross-lingual Semantic Parsing with UCCA’. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict …...

AbstractTransfer learning and domain adaptive learning have been applied to various fields including computer vision (e.g., image recognition) and natural language processing (e.g., text classification). One of the benefits of transfer learning is to learn effectively and efficiently from limited labeled data with a pre-trained model. In the shared task …...

AbstractThis paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA. We introduce a novel model by applying a cascaded MLP and BiLSTM model. Then, we ensemble multiple system-outputs by reparsing. In particular, we introduce a new decoding algorithm for building the …...

AbstractWe present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets, and it featured three sub-tasks. In sub-task A, …...

AbstractThe paper describes the organization of the SemEval 2019 Task 5 about the detection of hate speech against immigrants and women in Spanish and English messages extracted from Twitter. The task is organized in two related classification subtasks: a main binary subtask for detecting the presence of hate speech, and …...

AbstractThis paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. We participated in the subtask A for English and ranked first in the evaluation on the test set. We evaluate the quality of multiple sentence embeddings and …...

AbstractIn this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, …...

AbstractThis paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchi- cal LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational con- text. The results show that, in this task, our HRCLE outperforms …...

AbstractWe describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We …...

AbstractIn this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading “Why don’t you ever text me!” we can either interpret it as a …...

AbstractThis paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019. Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures. Results are useful in identifying polysemous words, i.e., …...

AbstractThis paper describes a simple UCCA semantic graph parsing approach. The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery. In this way, we can make use of existing …...

AbstractWe present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for …...

AbstractThis paper describes the graph transfor-mation system (GT System) for SemEval 2019 Task 1: Cross-lingual Semantic Parsing with Universal Conceptual Cognitive Annotation (UCCA)1. The input of GT System is a pair of text and its unannotated xml, which is a layer 0 part of UCCA form. The output of GT …...

AbstractWe present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, pre- …...