Semantic Similarity

257 papers with code • 7 benchmarks • 11 datasets

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Most implemented papers

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

UKPLab/sentence-transformers IJCNLP 2019

However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10, 000 sentences requires about 50 million inference computations (~65 hours) with BERT.

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

stanfordnlp/treelstm IJCNLP 2015

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.

ERNIE: Enhanced Representation through Knowledge Integration

PaddlePaddle/PaddleNLP 19 Apr 2019

We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).

Improving Language Understanding by Generative Pre-Training

huggingface/transformers Preprint 2018

We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.

Language-agnostic BERT Sentence Embedding

FreddeFrallan/Multilingual-CLIP ACL 2022

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored.

Calculating the similarity between words and sentences using a lexical database and corpus statistics

nihitsaxena95/sentence-similarity-wordnet-sementic 15 Feb 2018

To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database.

Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

shanzhenren/PLE 17 Feb 2016

Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.

Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

nathanshartmann/portuguese_word_embeddings WS 2017

Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems.

MedSTS: A Resource for Clinical Semantic Textual Similarity

ncbi-nlp/BioSentVec 28 Aug 2018

A subset of MedSTS (MedSTS_ann) containing 1, 068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0-5 (low to high similarity).