Acquisition of Turkish meronym based on classification of patterns

Yıldız, Tuğba; Di̇ri̇, Banu; Yıldırım, Savaş

doi:10.1007/s10044-015-0516-9

Cited by 9 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a final decision step, we pick the closest word in the dictionary to the predicted vector,ŷ ∈ R N . Some studies [23] used the K closest candidate words when evaluating the model. We use K = 1 and the closest word is taken as predicted.…”

Section: Resultsmentioning

confidence: 99%

Improving word embeddings projection for Turkish hypernym extraction

Yıldırım¹

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Corpus-driven approaches can automatically explore is-a relations between the word pairs from corpus. This problem is also called hypernym extraction. Formerly, lexico-syntactic patterns have been used to solve hypernym relations. The language-specific syntactic rules have been manually crafted to build the patterns. On the other hand, recent studies have applied distributional approaches to word semantics. They extracted the semantic relations relying on the idea that similar words share similar contexts. Former distributional approaches have applied one-hot bag-ofword (BOW) encoding. The dimensionality problem of BOW has been solved by various neural network approaches, which represent words in very short and dense vectors, or word embeddings. In this study, we used word embeddings representation and employed the optimized projection algorithm to solve the hypernym problem. The supervised architecture learns a mapping function so that the embeddings (or vectors) of word pairs that are in hypernym relations can be projected to each other. In the training phase, the architecture first learns the embeddings of words and the projection function from a list of word pairs. In the test phase, the projection function maps the embeddings of a given word to a point that is the closest to its hypernym. We utilized the deep learning optimization methods to optimize the model and improve the performances by tuning hyperparameters. We discussed our results by carrying out many experiments based on cross-validation. We also addressed problem-specific loss function, monitored hyperparameters, and evaluated the results with respect to different settings. Finally, we successfully showed that our approach outperformed baseline functions and other studies in the Turkish language.

show abstract

Section: Resultsmentioning

confidence: 99%

Improving word embeddings projection for Turkish hypernym extraction

Yıldırım¹

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Yildiz [13] proposed a semi-automatic method for extracting meronym-holonym pairs from Turkish corpus. The methodtakes some initial pairs as input and uses it to extract holonymy patterns.…”

Section: Holonymy Relationmentioning

confidence: 99%

Extraction of Hyponymy, Meronymy and Antonymy Relation Pairs : A Brief Survey

Sahin¹

2017

IJNLC

View full text Add to dashboard Cite

show abstract

“…For meronym relation extraction, some LSPs are categorized into 3 forms for the Turkish corpus: general patterns, dictionary-based patterns, and bootstrapped patterns, as proposed in a previous work [34]. All the details and examples of pattern specifications can be found in [34]. After applying these LSPs, some elimination assumptions and measurement metrics such as χ 2 or Pmi are utilized to acquire the meronym/holonym relation.…”

Section: Meronym/holonymmentioning

confidence: 99%

“…Meronym/holonym: The meronymy/holonymy relation is used to detect a synonymy relation. For meronym relation extraction, some LSPs are categorized into 3 forms for the Turkish corpus: general patterns, dictionary-based patterns, and bootstrapped patterns, as proposed in a previous work [34]. All the details and examples of pattern specifications can be found in [34].…”

Section: Cooccurrencementioning

confidence: 99%

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Yıldız¹,

Di̇ri̇²,

Yıldırım³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:In this study, a model is proposed to determine synonymy by incorporating several resources. The model extracts the features from monolingual online dictionaries, a bilingual online dictionary, WordNet, and a monolingual Turkish corpus. Once it has built a candidate list, it determines the synonymy for a given word by means of those features. All these resources and the approaches are evaluated. Taking all features into account and applying machine learning algorithms, the model shows good performance of F-measure with 81.4%. The study contributes to the literature by integrating several resources and attempting the first corpus-driven synonym detection system for Turkish.

show abstract

Acquisition of Turkish meronym based on classification of patterns

Cited by 9 publications

References 23 publications

Improving word embeddings projection for Turkish hypernym extraction

Improving word embeddings projection for Turkish hypernym extraction

Extraction of Hyponymy, Meronymy and Antonymy Relation Pairs : A Brief Survey

Turkish synonym identification from multiple resources: monolingual corpus, mono/bilingual online dictionaries, and WordNet

Contact Info

Product

Resources

About