On Learning Word Embeddings From Linguistically Augmented Text Corpora

2019-05-01 · WS 2019 5 · Amila Silva, Chathurika Amarathunga

Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Learning Word EmbeddingsWord EmbeddingsWord Similarity

Methods 이 논문이 사용한 방법론

GloVe GloVe Embeddings are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares…

Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models

2026-02-20 · Wojciech Michaluk, Tymoteusz Urban, Mateusz Kubita, Soveatin Kuntur 외 arxiv

Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with linguistically motivated in…

Addressing Noise in Multidialectal Word Embeddings

2018-07-01 · ACL 2018 7 · Alex Erdmann, er, Nasser Zalmout, Nizar Habash

Word embeddings are crucial to many natural language processing tasks. The quality of embeddings relies on large non-noisy corpora. Arabic dialects lack large corpora and are noisy, being linguistically disparate with no…

Sentence Transliteration Word Embeddings

LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity

2017-08-01 · SEMEVAL 2017 8 · Ignacio Arroyo-Fern{\'a}ndez, Ivan Vladimir Meza Ruiz

In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word ali…

Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations (EARP)

2018-10-01 · CONLL 2018 10 · Trevor Cohen, Dominic Widdows

Word order is clearly a vital part of human language, but it has been used comparatively lightly in distributional vector models. This paper presents a new method for incorporating word order information into word vector…

Retrieval Word Embeddings

Factor Augmented Supervised Learning with Text Embeddings

2025-08-06 · Zhanye Luo, Yuefeng Han, Xiufan Yu arxiv

Large language models (LLMs) generate text embeddings from text data, producing vector representations that capture the semantic meaning and contextual relationships of words. However, the high dimensionality of these em…

Anomaly Detection