Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining

Qiu, Likun; Zhang, Yue; Lu, Yanan

doi:10.18653/v1/d15-1291

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…Embedding evaluation was performed on Chinese WordSim (CWS), a transcription from English WS-353 by two undergraduate students with excellent English understandings (Qiu, Zhang, & Lu, 2015). The similarity scores containing in CWS are rescored by 20 native Chinese speakers and used in Semeval-2012 task 4 (Jin & Wu, 2012).…”

Section: Abnormal Dimensions In Chinese Word Embeddingmentioning

confidence: 99%

Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

Lee

Hao

Yen

et al. 2019

Asso for Info Science & Tech

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In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state-of-the-art approaches. The experimental results show that our method can outperform state-of-the-art approaches in all the selected English benchmark data sets.

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Section: Abnormal Dimensions In Chinese Word Embeddingmentioning

confidence: 99%

Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

Lee

Hao

Yen

et al. 2019

Asso for Info Science & Tech

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show abstract

“…Since distributed representations play an important role in various NLP tasks, they are applied to semantics (Herbelot and Vecchi, 2015;Qiu et al, 2015;Woodsend and Lapata, 2015), with incorporating external information to them (Tian et al, 2016;Nguyen et al, 2016). In addition, finding interpretable regularities from the representations is often conducted through non-negative and sparse coding (Murphy et al, 2012;Faruqui et al, 2015;Luo et al, 2015;Kober et al, 2016), and regularization (Sun et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Rotated Word Vector Representations and their Interpretability

Park¹,

Bak²,

Oh³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Vector representation of words improves performance in various NLP tasks, but the high-dimensional word vectors are very difficult to interpret. We apply several rotation algorithms to the vector representation of words to improve the interpretability. Unlike previous approaches that induce sparsity, the rotated vectors are interpretable while preserving the expressive performance of the original vectors. Furthermore, any pre-built word vector representation can be rotated for improved interpretability. We apply rotation to skipgrams and glove and compare the expressive power and interpretability with the original vectors and the sparse overcomplete vectors. The results show that the rotated vectors outperform the original and the sparse overcomplete vectors for interpretability and expressiveness tasks.

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“…Le and Mikolov (Le and Mikolov, 2014) proposed an unsupervised algorithm that learns fixed-length feature representations from variable-length texts. Qiu et al (Qiu et al, 2015) explored distributed representations of words to detect analogies. In this paper, we exploit the distributed representation approach to transform item descriptions to vectors, and assist recommendation based on these vectors.…”

Section: Related Workmentioning

confidence: 99%

Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions

He¹,

Zhuo²,

Law³

2017

Preprint

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Collaborative filtering (CF) aims to build a model from users' past behaviors and/or similar decisions made by other users, and use the model to recommend items for users. Despite of the success of previous collaborative filtering approaches, they are all based on the assumption that there are sufficient rating scores available for building high-quality recommendation models. In real world applications, however, it is often difficult to collect sufficient rating scores, especially when new items are introduced into the system, which makes the recommendation task challenging. We find that there are often "short" texts describing features of items, based on which we can approximate the similarity of items and make recommendation together with rating scores. In this paper we "borrow" the idea of vector representation of words to capture the information of short texts and embed it into a matrix factorization framework. We empirically show that our approach is effective by comparing it with state-of-the-art approaches.

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Syntactic Dependencies and Distributed Word Representations for Analogy Detection and Mining

Cited by 8 publications

References 17 publications

Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

Rotated Word Vector Representations and their Interpretability

Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions

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