99 citations · 116 across the 10 of their papers we have counts for
13 papers
UCCA's Foundational Layer: Annotation Guidelines v2.1
Omri Abend, Nathan Schneider, Dotan Dvir +2
This is the annotation manual for Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013), specifically the Foundational Layer. UCCA is a graph-based semantic a…
Content Differences in Syntactic and Semantic Representations
Daniel Hershcovich, Omri Abend, Ari Rappoport
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of em…
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
Daniel Hershcovich, Zohar Aizenbud, Leshem Choshen +3
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable…
BLEU is Not Suitable for the Evaluation of Text Simplification
Elior Sulem, Omri Abend, Ari Rappoport
BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this pape…
Simple and Effective Text Simplification Using Semantic and Neural Methods
Elior Sulem, Omri Abend, Ari Rappoport
Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text i…
Semantic Structural Evaluation for Text Simplification
Elior Sulem, Omri Abend, Ari Rappoport
Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure…
Universal Dependency Parsing with a General Transition-Based DAG Parser
Daniel Hershcovich, Omri Abend, Ari Rappoport
This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first exp…
Computing Word Classes Using Spectral Clustering
Effi Levi, Saggy Herman, Ari Rappoport
Clustering a lexicon of words is a well-studied problem in natural language processing (NLP). Word clusters are used to deal with sparse data in statistical language processing, as…
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Daniel Hershcovich, Leshem Choshen, Elior Sulem +3
We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for…
Multitask Parsing Across Semantic Representations
Daniel Hershcovich, Omri Abend, Ari Rappoport
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from gener…
A Transition-Based Directed Acyclic Graph Parser for UCCA
Daniel Hershcovich, Omri Abend, Ari Rappoport
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotat…
Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
Ivan Vulić, Roy Schwartz, Ari Rappoport +2
This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We int…
Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference
Effi Levi, Roi Reichart, Ari Rappoport
The run time complexity of state-of-the-art inference algorithms in graph-based dependency parsing is super-linear in the number of input words (n). Recently, pruning algorithms fo…