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NLP-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Taxonomy Learning

Taxonomy learning is the task of hierarchically classifying concepts in an automatic manner from text corpora. The process of building taxonomies is usually divided into two main steps: (1) extracting hypernyms for concepts, which may constitute a field of research in itself (see Hypernym Discovery below) and (2) refining the structure into a taxonomy.

Hypernym Discovery

Given a corpus and a target term (hyponym), the task of hypernym discovery consists of extracting a set of its most appropriate hypernyms from the corpus. For example, for the input word “dog”, some valid hypernyms would be “canine”, “mammal” or “animal”.

SemEval 2018

The SemEval-2018 hypernym discovery evaluation benchmark (Camacho-Collados et al. 2018), which can be freely downloaded here, contains three domains (general, medical and music) and is also available in Italian and Spanish (not in this repository). For each domain a target corpus and vocabulary (i.e. hypernym search space) are provided. The dataset contains both concepts (e.g. dog) and entities (e.g. Manchester United) up to trigrams. The following table lists the number of hyponym-hypernym pairs for each dataset:

Partition General Medical Music
Trial 200 101 355
Training 11779 3256 5455
Test 7048 4116 5233

The results for each model and dataset (general, medical and music) are presented below (MFH stands for “Most Frequent Hypernyms” and is used as a baseline).

General:

Model MAP MRR P@5 Paper / Source
CRIM (Bernier-Colborne and Barrière, 2018) 19.78 36.10 19.03 A Hybrid Approach to Hypernym Discovery
vTE (Espinosa-Anke et al., 2016) 10.60 23.83 9.91 Supervised Distributional Hypernym Discovery via Domain Adaptation
NLP_HZ (Qui et al., 2018) 9.37 17.29 9.19 A Nearest Neighbor Approach
300-sparsans (Berend et al., 2018) 8.95 19.44 8.63 Hypernymy as interaction of sparse attributes
MFH 8.77 21.39 7.81
SJTU BCMI (Zhang et al., 2018) 5.77 10.56 5.96 Neural Hypernym Discovery with Term Embeddings
Apollo (Onofrei et al., 2018) 2.68 6.01 2.69 Detecting Hypernymy Relations Using Syntactic Dependencies
balAPInc (Shwartz et al., 2017) 1.36 3.18 1.30 Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

Medical domain:

Model MAP MRR P@5 Paper / Source
CRIM (Bernier-Colborne and Barrière, 2018) 34.05 54.64 36.77 A Hybrid Approach to Hypernym Discovery
MFH 28.93 35.80 34.20
300-sparsans (Berend et al., 2018) 20.75 40.60 21.43 Hypernymy as interaction of sparse attributes
vTE (Espinosa-Anke et al., 2016) 18.84 41.07 20.71 Supervised Distributional Hypernym Discovery via Domain Adaptation
EXPR (Issa Alaa Aldine et al., 2018) 13.77 40.76 12.76 A Combined Approach for Hypernym Discovery
SJTU BCMI (Zhang et al., 2018) 11.69 25.95 11.69 Neural Hypernym Discovery with Term Embeddings
ADAPT (Maldonado and Klubička, 2018) 8.13 20.56 8.32 Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora
balAPInc (Shwartz et al., 2017) 0.91 2.10 1.08 Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

Music domain:

Model MAP MRR P@5 Paper / Source
CRIM (Bernier-Colborne and Barrière, 2018) 40.97 60.93 41.31 A Hybrid Approach to Hypernym Discovery
MFH 33.32 51.48 35.76
300-sparsans (Berend et al., 2018) 29.54 46.43 28.86 Hypernymy as interaction of sparse attributes
vTE (Espinosa-Anke et al., 2016) 12.99 39.36 12.41 Supervised Distributional Hypernym Discovery via Domain Adaptation
SJTU BCMI (Zhang et al., 2018) 4.71 9.15 4.91 Neural Hypernym Discovery with Term Embeddings
ADAPT (Maldonado and Klubička, 2018) 2.63 7.46 2.64 Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora
balAPInc (Shwartz et al., 2017) 1.95 5.01 2.15 Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection

Taxonomy Enrichment

Given words that are not included in a taxonomy, the task is to associate each query word with its appropriate hypernyms. For instance, given an input word “milk” we need to provide a list of the most probable hypernyms the word could be attached to, e.g. “dairy product”, “beverage”. A word may have multiple hypernyms.

Datasets

SemEval 2016 Task 14

The SemEval-2016 Task 14 aims to enrich the WordNet taxonomy with new words and word senses. A system’s task is to identify the WordNet synset with which the new word sense should be merged (i.e., the term is synonymous with those in the synset) or added as a hyponym (i.e., the new word sense is a specialization of an exisiting word sense).

The following table gives examples of word senses that are not defined in WordNet and their corresponding operations, illustrating the type of data that might be seen in the task.

OOV word Definition Target synset Operation
geoscience#n any of several sciences that deal with the Earth earth_science (any of the sciences that deal with the earth or its parts) MERGE
mudslide#n a mixed drink consisting of vodka, Kahlua and Bailey’s. cocktail (a short mixed drink) ATTACH
euthanize#v to submit or animal to euthanasia destroy, put down (put (an animal) to death) MERGE

The SemEval-2016 taxonomy enrichment evaluation benchmark (Jurgens and Pilehvar 2016), which can be freely downloaded here.

Novel concepts were limited to nouns and verbs, as only these parts of speech have fully-developed taxonomies in WordNet. For each item, in addition to the target synset and the operation MERGE/ATTACH, the glosses were also provided along with the source URL from which the new word sense was obtained. The dataset consists of a total of 1000 items, split into training and test datasets containing 400 and 600 items, respectively. The following table lists the number of items for each dataset:

Partition Noun Verb
Trial 93 34
Training 349 51
Test 516 84

The results for each model participant are presented below.

Model Lemma Match Wu&P Recall F1 Paper / Source
MSejrKU (Schlichtkrull and Alonso, 2016) 0.428 0.523 0.973 0.680 MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking
TALN (Anke et al., 2016) 0.360 0.476 1.000 0.645 Semantic Taxonomy Enrichment Via Sense-Based Embeddings
VCU (McInnes, 2016) 0.161 0.432 0.997 0.602 Evaluating definitional-based similarity measure for semantic taxonomy enrichment
Duluth (Pedersen, 2016) 0.043 0.347 1.000 0.515 Extending Gloss Overlaps to Enrich Semantic Taxonomies
Deftor (Tanev and Rotondi, 2016) 0.066 0.347 0.987 0.513 Taxonomy Enrichment using Definition Vectors
UMNDuluth (Rusert and Pedersen, 2016) 0.098 0.340 0.998 0.507 WordNet’s Missing Lemmas
Baseline: First word, first sense (Jurgens and Pilehvar, 2016) 0.415 0.514 1.000 0.679 SemEval-2016 Task 14: Semantic Taxonomy Enrichment
Baseline: Random synset (Jurgens and Pilehvar, 2016) 0.000 0.227 1.000 0.370 SemEval-2016 Task 14: Semantic Taxonomy Enrichment

Diachronic WordNet Datasets

The SemEval-2016 Task 14 setting implies pre-defined glosses. However, it is possible that new words that should be added to the taxonomy may have no definition in any other sources: they could be very rare (“apparatchik”, “falanga”), relatively new (“selfie”, “hashtag”) or come from a narrow domain (“vermiculite”).

Nikishina et al., 2020 created multiple datasets for studying diachronic evolution of wordnets, which can be downloaded from here. They chose two versions of WordNet and then select words which appear only in a newer version. For each word, they got its hypernyms from the newer WordNet version and consider them as gold standard hypernyms. The words were added to the dataset if only their hypernyms appear in both snippets. They skipped one or more WordNet versions, otherwise the dataset would be too small.

Dataset Noun Verb
WordNet 1.6 - WordNet 3.0 17 043 755
WordNet 1.7 - WordNet 3.0 6 161 362
WordNet 2.0 - WordNet 3.0 2 620 193

The results for each system on the current dataset are presented below.

WordNet 1.6 - WordNet 3.0
Model MAP (Nouns) MAP (Verbs) Paper / Source
AAEME triplet loss (Tikhomirov and Loukachevitch, 2021) 0.345 0.289 Meta-Embeddings in Taxonomy Enrichment Task
Ranking + Wiki (Nikishina et al., 2020) 0.337 0.270 Studying Taxonomy Enrichment on Diachronic WordNet Versions
Ranking + Wiki + node2vec + Poincare (Nikishina et al., 2021) 0.311 0.251 Exploring Graph-based Representations for Taxonomy Enrichment
WordNet 1.7 - WordNet 3.0
Model MAP (Nouns) MAP (Verbs) Paper / Source
AAEME triplet loss (Tikhomirov and Loukachevitch, 2021) 0.394 0.239 Meta-Embeddings in Taxonomy Enrichment Task
Ranking + Wiki (Nikishina et al., 2020) 0.380 0.200 Studying Taxonomy Enrichment on Diachronic WordNet Versions
Ranking + Wiki + node2vec + Poincare (Nikishina et al., 2021) 0.350 0.177 Exploring Graph-based Representations for Taxonomy Enrichment
WordNet 2.0 - WordNet 3.0
Model MAP (Nouns) MAP (Verbs) Paper / Source
AAEME triplet loss (Tikhomirov and Loukachevitch, 2021) 0.445 0.272 Meta-Embeddings in Taxonomy Enrichment Task
Ranking + Wiki (Nikishina et al., 2020) 0.400 0.238 Studying Taxonomy Enrichment on Diachronic WordNet Versions
Ranking + Wiki + node2vec + Poincare (Nikishina et al., 2021) 0.300 0.248 Exploring Graph-based Representations for Taxonomy Enrichment