81 research outputs found
Discriminating between lexico-semantic relations with the specialization tensor model
We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a lingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data
Recommended from our members
Scoring lexical entailment with a supervised directional similarity network
Scoring Lexical Entailment with a Supervised Directional Similarity NetworkERC
Nvidi
Scoring lexical entailment with a supervised directional similarity network
Scoring Lexical Entailment with a Supervised Directional Similarity NetworkERC
Nvidi
Political Text Scaling Meets Computational Semantics
During the last fifteen years, automatic text scaling has become one of the
key tools of the Text as Data community in political science. Prominent text
scaling algorithms, however, rely on the assumption that latent positions can
be captured just by leveraging the information about word frequencies in
documents under study. We challenge this traditional view and present a new,
semantically aware text scaling algorithm, SemScale, which combines recent
developments in the area of computational linguistics with unsupervised
graph-based clustering. We conduct an extensive quantitative analysis over a
collection of speeches from the European Parliament in five different languages
and from two different legislative terms, and show that a scaling approach
relying on semantic document representations is often better at capturing known
underlying political dimensions than the established frequency-based (i.e.,
symbolic) scaling method. We further validate our findings through a series of
experiments focused on text preprocessing and feature selection, document
representation, scaling of party manifestos, and a supervised extension of our
algorithm. To catalyze further research on this new branch of text scaling
methods, we release a Python implementation of SemScale with all included data
sets and evaluation procedures.Comment: Updated version - accepted for Transactions on Data Science (TDS
Unsupervised Sense-Aware Hypernymy Extraction
In this paper, we show how unsupervised sense representations can be used to
improve hypernymy extraction. We present a method for extracting disambiguated
hypernymy relationships that propagates hypernyms to sets of synonyms
(synsets), constructs embeddings for these sets, and establishes sense-aware
relationships between matching synsets. Evaluation on two gold standard
datasets for English and Russian shows that the method successfully recognizes
hypernymy relationships that cannot be found with standard Hearst patterns and
Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing
(KONVENS 2018). Vienna, Austri
Improving Hypernymy Extraction with Distributional Semantic Classes
In this paper, we show how distributionally-induced semantic classes can be
helpful for extracting hypernyms. We present methods for inducing sense-aware
semantic classes using distributional semantics and using these induced
semantic classes for filtering noisy hypernymy relations. Denoising of
hypernyms is performed by labeling each semantic class with its hypernyms. On
the one hand, this allows us to filter out wrong extractions using the global
structure of distributionally similar senses. On the other hand, we infer
missing hypernyms via label propagation to cluster terms. We conduct a
large-scale crowdsourcing study showing that processing of automatically
extracted hypernyms using our approach improves the quality of the hypernymy
extraction in terms of both precision and recall. Furthermore, we show the
utility of our method in the domain taxonomy induction task, achieving the
state-of-the-art results on a SemEval'16 task on taxonomy induction.Comment: In Proceedings of the 11th Conference on Language Resources and
Evaluation (LREC 2018). Miyazaki, Japa
Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection
Modeling hypernymy, such as poodle is-a dog, is an important generalization
aid to many NLP tasks, such as entailment, coreference, relation extraction,
and question answering. Supervised learning from labeled hypernym sources, such
as WordNet, limits the coverage of these models, which can be addressed by
learning hypernyms from unlabeled text. Existing unsupervised methods either do
not scale to large vocabularies or yield unacceptably poor accuracy. This paper
introduces distributional inclusion vector embedding (DIVE), a
simple-to-implement unsupervised method of hypernym discovery via per-word
non-negative vector embeddings which preserve the inclusion property of word
contexts in a low-dimensional and interpretable space. In experimental
evaluations more comprehensive than any previous literature of which we are
aware-evaluating on 11 datasets using multiple existing as well as newly
proposed scoring functions-we find that our method provides up to double the
precision of previous unsupervised embeddings, and the highest average
performance, using a much more compact word representation, and yielding many
new state-of-the-art results.Comment: NAACL 201
- …