Multiplex Word Embeddings for Selectional Preference Acquisition
- URL: http://arxiv.org/abs/2001.02836v1
- Date: Thu, 9 Jan 2020 04:47:14 GMT
- Title: Multiplex Word Embeddings for Selectional Preference Acquisition
- Authors: Hongming Zhang, Jiaxin Bai, Yan Song, Kun Xu, Changlong Yu, Yangqiu
Song, Wilfred Ng and Dong Yu
- Abstract summary: We propose a multiplex word embedding model, which can be easily extended according to various relations among words.
Our model can effectively distinguish words with respect to different relations without introducing unnecessary sparseness.
- Score: 70.33531759861111
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conventional word embeddings represent words with fixed vectors, which are
usually trained based on co-occurrence patterns among words. In doing so,
however, the power of such representations is limited, where the same word
might be functionalized separately under different syntactic relations. To
address this limitation, one solution is to incorporate relational dependencies
of different words into their embeddings. Therefore, in this paper, we propose
a multiplex word embedding model, which can be easily extended according to
various relations among words. As a result, each word has a center embedding to
represent its overall semantics, and several relational embeddings to represent
its relational dependencies. Compared to existing models, our model can
effectively distinguish words with respect to different relations without
introducing unnecessary sparseness. Moreover, to accommodate various relations,
we use a small dimension for relational embeddings and our model is able to
keep their effectiveness. Experiments on selectional preference acquisition and
word similarity demonstrate the effectiveness of the proposed model, and a
further study of scalability also proves that our embeddings only need 1/20 of
the original embedding size to achieve better performance.
Related papers
- Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching [10.709744162565274]
We propose a novel method called DIAS to bridge the modality gap from two aspects.
The method achieves 4.3%-10.2% rSum improvements on Flickr30k and MSCOCO benchmarks.
arXiv Detail & Related papers (2024-10-22T09:37:29Z) - Meaning Representations from Trajectories in Autoregressive Models [106.63181745054571]
We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text.
This strategy is prompt-free, does not require fine-tuning, and is applicable to any pre-trained autoregressive model.
We empirically show that the representations obtained from large models align well with human annotations, outperform other zero-shot and prompt-free methods on semantic similarity tasks, and can be used to solve more complex entailment and containment tasks that standard embeddings cannot handle.
arXiv Detail & Related papers (2023-10-23T04:35:58Z) - Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings.
RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
arXiv Detail & Related papers (2022-12-17T05:25:17Z) - Cross-Domain Few-Shot Relation Extraction via Representation Learning
and Domain Adaptation [1.1602089225841632]
Few-shot relation extraction aims to recognize novel relations with few labeled sentences in each relation.
Previous metric-based few-shot relation extraction algorithms identify relationships by comparing the prototypes generated by the few labeled sentences embedding with the embeddings of the query sentences using a trained metric function.
We suggest learning more interpretable and efficient prototypes from prior knowledge and the intrinsic semantics of relations to extract new relations in various domains more effectively.
arXiv Detail & Related papers (2022-12-05T19:34:52Z) - The distribution of syntactic dependency distances [0.7614628596146599]
We contribute to the characterization of the actual distribution of syntactic dependency distances.
We propose a new double-exponential model in which decay in probability is allowed to change after a break-point.
We find that a two-regime model is the most likely one in all 20 languages we considered.
arXiv Detail & Related papers (2022-11-26T17:31:25Z) - LexSubCon: Integrating Knowledge from Lexical Resources into Contextual
Embeddings for Lexical Substitution [76.615287796753]
We introduce LexSubCon, an end-to-end lexical substitution framework based on contextual embedding models.
This is achieved by combining contextual information with knowledge from structured lexical resources.
Our experiments show that LexSubCon outperforms previous state-of-the-art methods on LS07 and CoInCo benchmark datasets.
arXiv Detail & Related papers (2021-07-11T21:25:56Z) - Prototypical Representation Learning for Relation Extraction [56.501332067073065]
This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data.
We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations.
Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
arXiv Detail & Related papers (2021-03-22T08:11:43Z) - Multidirectional Associative Optimization of Function-Specific Word
Representations [86.87082468226387]
We present a neural framework for learning associations between interrelated groups of words.
Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together.
The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure.
arXiv Detail & Related papers (2020-05-11T17:07:20Z) - Comparative Analysis of Word Embeddings for Capturing Word Similarities [0.0]
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks.
Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings.
selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.
arXiv Detail & Related papers (2020-05-08T01:16:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.