Papers Hypernym Discovery

OntoTune: Ontology-Driven Self-training for Aligning Large Language Models

Existing domain-specific Large Language Models (LLMs) are typically developed by fine-tuning general-purposed LLMs with large-scale domain-specific corpora. However, training on large-scale corpora often fails to effecti…

TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical semantic tasks. As the outcome of our experim…

Probing Pretrained Language Models with Hierarchy Properties

Since Pretrained Language Models (PLMs) are the cornerstone of the most recent Information Retrieval (IR) models, the way they encode semantic knowledge is particularly important. However, little attention has been given…

Modelling Commonsense Properties using Pre-Trained Bi-Encoders

Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding. While contextualised language models are reportedly capable of predicting such commonsense properties with …

HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings

Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which …

CogALex 2.0: Impact of Data Quality on Lexical-Semantic Relation Prediction

Predicting lexical-semantic relations between word pairs has successfully been accomplished by pre-trained neural language models. An XLM-RoBERTa-based approach, for instance, achieved the best performance differentiatin…

Analyzing BERT’s Knowledge of Hypernymy via Prompting

The high performance of large pretrained language models (LLMs) such as BERT on NLP tasks has prompted questions about BERT’s linguistic capabilities, and how they differ from humans’. In this paper, we approach this que…

Hypernym Discovery via a Recurrent Mapping Model

Document Structure aware Relational Graph Convolutional Networks for Ontology Population

Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems. While there have been efforts towards populating domain specific ontologies, we examine the role of docu…

CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification

We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model. In spite of several…

Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings

Word embeddings have become a standard resource in the toolset of any Natural Language Processing practitioner. While monolingual word embeddings encode information about words in the context of a particular language, cr…

The Effectiveness of Simple Hybrid Systems for Hypernym Discovery

Hypernymy modeling has largely been separated according to two paradigms, pattern-based methods and distributional methods. However, recent works utilizing a mix of these strategies have yielded state-of-the-art results.…

Document Structure Measure for Hypernym discovery

Hypernym discovery is the problem of finding terms that have is-a relationship with a given term. We introduce a new context type, and a relatedness measure to differentiate hypernyms from other types of semantic relatio…

Learning Scalar Adjective Intensity from Paraphrases

Adjectives like {``}warm{''}, {``}hot{''}, and {``}scalding{''} all describe temperature but differ in intensity. Understanding these differences between adjectives is a necessary part of reasoning about natural language…

SemEval-2018 Task 9: Hypernym Discovery

This paper describes the SemEval 2018 Shared Task on Hypernym Discovery. We put forward this task as a complementary benchmark for modeling hypernymy, a problem which has traditionally been cast as a binary classificatio…

CRIM at SemEval-2018 Task 9: A Hybrid Approach to Hypernym Discovery

This report describes the system developed by the CRIM team for the hypernym discovery task at SemEval 2018. This system exploits a combination of supervised projection learning and unsupervised pattern-based hypernym di…

Apollo at SemEval-2018 Task 9: Detecting Hypernymy Relations Using Syntactic Dependencies

This paper presents the participation of Apollo{'}s team in the SemEval-2018 Task 9 {``}Hypernym Discovery{''}, Subtask 1: {``}General-Purpose Hypernym Discovery{''}, which tries to produce a ranked list of hypernyms for…

NLP\_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach

Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets based on word embedding, which can b…

UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings

Hypernym Discovery is the task of identifying potential hypernyms for a given term. A hypernym is a more generalized word that is super-ordinate to more specific words. This paper explores several approaches that rely on…

EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery

In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a…