1 |
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
|
|
BASE
|
|
Show details
|
|
2 |
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
On the Limitations of Unsupervised Bilingual Dictionary Induction ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Scoring Lexical Entailment with a Supervised Directional Similarity Network ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
A deep learning approach to bilingual lexicon induction in the biomedical domain ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Investigating the cross-lingual translatability of VerbNet-style classification. ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Specialising Word Vectors for Lexical Entailment ...
|
|
|
|
Abstract:
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model. ...
|
|
URL: https://dx.doi.org/10.17863/cam.41175 https://www.repository.cam.ac.uk/handle/1810/294075
|
|
BASE
|
|
Hide details
|
|
17 |
A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
A deep learning approach to bilingual lexicon induction in the biomedical domain.
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
|
|
|
|
BASE
|
|
Show details
|
|
|
|