Roi Reichart

Automatic Selection of Context Configurations for Improved Class-Specific Word Representations

2017

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 introduce a simple yet effective framework for an …

Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity

2020 · Computational Linguistics

We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each …

PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

2021 · arXiv (Cornell University)

Natural Language Processing algorithms have made incredible progress, but they still struggle when applied to out-of-distribution examples. We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on …

Leveraging Prompt-Learning for Structured Information Extraction from Crohn’s Disease Radiology Reports in a Low-Resource Language

2024

Liam Hazan, Naama Gavrielov, Roi Reichart, Talar Hagopian, Mary-Louise Greer, Ruth Cytter-Kuint, Gili Focht, Dan Turner, Moti Freiman. Proceedings of the 6th Clinical Natural Language Processing Workshop. 2024.

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

2015

We present a novel word level vector representation based on symmetric patterns (SPs). For this aim we automatically acquire SPs (e.g., "X and Y") from a large corpus of plain text, and generate vectors where …

SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation

2015 · Computational Linguistics

We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly quantifies …

Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints

2017 · Transactions of the Association for Computational Linguistics

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual …

The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing

2018

Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. In this opinion/theoretical paper we discuss the role of statistical significance testing in Natural Language Processing (NLP) research. …

Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP

2018

The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal …

Pivot Based Language Modeling for Improved Neural Domain Adaptation

2018

Representation learning with pivot-based methods and with Neural Networks (NNs) have lead to significant progress in domain adaptation for Natural Language Processing. However, most previous work that follows these approaches does not explicitly exploit the …

On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling

2018

A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological …