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16 dataset results for Semantic Similarity

CoNLL 2003

CoNLL-2003 is a named entity recognition dataset released as a part of CoNLL-2003 shared task: language-independent named entity recognition. The data consists of eight files covering two languages: English and German. For each of the languages there is a training file, a development file, a test file and a large file with unannotated data.

747 PAPERS • 19 BENCHMARKS

SICK (Sentences Involving Compositional Knowledge)

The Sentences Involving Compositional Knowledge (SICK) dataset is a dataset for compositional distributional semantics. It includes a large number of sentence pairs that are rich in the lexical, syntactic and semantic phenomena. Each pair of sentences is annotated in two dimensions: relatedness and entailment. The relatedness score ranges from 1 to 5, and Pearson’s r is used for evaluation; the entailment relation is categorical, consisting of entailment, contradiction, and neutral. There are 4439 pairs in the train split, 495 in the trial split used for development and 4906 in the test split. The sentence pairs are generated from image and video caption datasets before being paired up using some algorithm.

341 PAPERS • 5 BENCHMARKS

BIOSSES (Biomedical Semantic Similarity Estimation System)

The BIOSSES data set comprises total 100 sentence pairs all of which were selected from the "TAC2 Biomedical Summarization Track Training Data Set" .

37 PAPERS • 3 BENCHMARKS

MultiFC

Publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists.

21 PAPERS • NO BENCHMARKS YET

CHIP-STS

CHIP-STS (Semantic Textual Similarity Dataset)

CHIP Semantic Textual Similarity, a dataset for sentence similarity in the non-i.i.d. (non-independent and identically distributed) setting, is used for the CHIP-STS task. Specifically, the task aims to transfer learning between disease types on Chinese disease questions and answer data. Given question pairs related to 5 different diseases (The disease types in the training and testing set are different), the task intends to determine whether the semantics of the two sentences are similar.

8 PAPERS • 1 BENCHMARK

SV-Ident (Survey Variable Identification)

SV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Sentences are labeled with variables that are mentioned either explicitly or implicitly.

4 PAPERS • 2 BENCHMARKS

COS960

A benchmark dataset with 960 pairs of Chinese wOrd Similarity, where all the words have two morphemes in three Part of Speech (POS) tags with their human annotated similarity rather than relatedness.

3 PAPERS • NO BENCHMARKS YET

SpokenSTS

Spoken versions of the Semantic Textual Similarity dataset for testing semantic sentence level embeddings. Contains thousands of sentence pairs annotated by humans for semantic similarity. The spoken sentences can be used in sentence embedding models to test whether your model learns to capture sentence semantics. All sentences available in 6 synthetic Wavenet voices and a subset (5%) in 4 real voices recorded in a sound attenuated booth. Code to train a visually grounded spoken sentence embedding model and evaluation code is available at https://github.com/DannyMerkx/speech2image/tree/Interspeech21

3 PAPERS • NO BENCHMARKS YET

SugarCrepe++

The SUGARCREPE++ dataset evaluates the sensitivity of vision language models (VLMs) and unimodal language models (ULMs) to semantic and lexical alterations. Each sample in the SugarCrepe++ dataset consists of an image and a corresponding triplet of captions: a pair of semantically equivalent but lexically different positive captions and one hard negative caption. This poses a 3-way semantic (in)equivalence problem to the language models. The SUGARCREPE dataset consists of (only) one positive and one hard negative caption for each image. Relative to the negative caption, a single positive caption can either have low or high lexical overlap. The original SUGARCREPE only captures the high overlap case. To evaluate the sensitivity of encoded semantics to lexical alteration, we require an additional positive caption with a different lexical composition. SUGARCREPE++ fills this gap by adding an additional positive caption enabling a more thorough assessment of models’ abilities to handle se

3 PAPERS • NO BENCHMARKS YET

Japanese Word Similarity

This dataset contains information about Japanese word similarity including rare words. The dataset is constructed following the Stanford Rare Word Similarity Dataset. 10 annotators annotated word pairs with 11 levels of similarity.

2 PAPERS • NO BENCHMARKS YET

LatamXIX

LatamXIX (19th Century Latin American Spanish Newspaper Corpus with LLM OCR Correction)

A novel dataset of 19th-century Latin American press texts, which addresses the lack of specialized corpora for historical and linguistic analysis in this region.

2 PAPERS • NO BENCHMARKS YET

Semantic Question Similarity in Arabic

Semantic Question Similarity in Arabic (NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic)

NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic

2 PAPERS • NO BENCHMARKS YET

Names pairs dataset

Includes co-referent name string pairs along with their similarities.

1 PAPER • NO BENCHMARKS YET

Phrase-in-Context

Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0.

1 PAPER • NO BENCHMARKS YET

Spanish Corpus XIX

Spanish Corpus XIX (19th Century Spanish Corpus)

Click to add a brief description of the dataset (Markdown and LaTeX enabled).

1 PAPER • NO BENCHMARKS YET

Speech Brown

Dataset Summary Speech Brown is a comprehensive, synthetic, and diverse paired speech-text dataset in 15 categories, covering a wide range of topics from fiction to religion. This dataset consists of over 55,000 sentence-level samples.

1 PAPER • NO BENCHMARKS YET

Datasets

16 dataset results for Semantic Similarity