Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification

Roth, Michael; Walde, Sabine Schulte im

doi:10.3115/v1/p14-2086

Cited by 41 publications

(16 citation statements)

References 30 publications

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“…A useful property of our model is its ability to control the representation of antonym pairs. Outside the VSM literature several works identified antonyms using word co-occurrence statistics, manually and automatically induced patterns, the WordNet lexicon and thesauri (Lin et al, 2003;Turney, 2008;Wang et al, 2010;Mohammad et al, 2013;Schulte im Walde and Koper, 2013;Roth and Schulte im Walde, 2014). Recently, Yih et al (2012), Chang et al (2013) and Ono et al (2015) proposed word representation methods that assign dissimilar vectors to antonyms.…”

Section: Related Workmentioning

confidence: 99%

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

Schwartz¹,

Reichart²,

Rappoport³

2015

Proceedings of the Nineteenth Conference on Computational Natural Language Learning

100

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We present a novel word level vector representation based on symmetric patterns (SPs). For this aim we automatically acquire SPs (e.g., "X and Y") from a large corpus of plain text, and generate vectors where each coordinate represents the cooccurrence in SPs of the represented word with another word of the vocabulary. Our representation has three advantages over existing alternatives: First, being based on symmetric word relationships, it is highly suitable for word similarity prediction. Particularly, on the SimLex999 word similarity dataset, our model achieves a Spearman's ρ score of 0.517, compared to 0.462 of the state-of-the-art word2vec model. Interestingly, our model performs exceptionally well on verbs, outperforming stateof-the-art baselines by 20.2-41.5%. Second, pattern features can be adapted to the needs of a target NLP application. For example, we show that we can easily control whether the embeddings derived from SPs deem antonym pairs (e.g. (big,small)) as similar or dissimilar, an important distinction for tasks such as word classification and sentiment analysis. Finally, we show that a simple combination of the word similarity scores generated by our method and by word2vec results in a superior predictive power over that of each individual model, scoring as high as 0.563 in Spearman's ρ on SimLex999. This emphasizes the differences between the signals captured by each of the models.

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Section: Related Workmentioning

confidence: 99%

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

Schwartz¹,

Reichart²,

Rappoport³

2015

Proceedings of the Nineteenth Conference on Computational Natural Language Learning

100

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“…Paradigmatic relations such as synonymy, antonymy and hypernymy are notoriously difficult to distinguish because distributions of context words tend to be very similar across the relations [4,5]. An interesting example in [6], with regard to the sentence The boy/girl/person loves/hates the cat, the nominal co-hyponyms boy, girl and their hypernym person as well as the verbal antonyms love and hate occur in identical contexts, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Efficient Antonymy-Synonymy Classification by Exploiting Co-occurrence Contexts and Word-Structure Patterns

Bui¹,

Nguyen²,

Pham³

et al. 2020

IJIES

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Antonymy and synonymy are basic semantic relations between words. Automatically distinguishing between antonymy and synonymy is an important task in natural language processing. This task is hard because antonyms and synonyms tend to occur in highly similar contexts. Recent studies often focus on exploiting densevector representations of words to deal with this problem. In this paper, we present a study on antonymy-synonymy discrimination for the Vietnamese language. We propose a deep neural network model (DVASNet) that can utilize not only embedding representations of words but also co-occurrence contexts and specific patterns of Vietnamese word structure. Our experimental results showed that the proposed method achieved significant improvements in comparison with a number of state-of-the-art methods by 14% to 17% in terms of F1 score for both benchmark datasets.

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“…Lin et al (2003) proposed two textual patterns indicating semantic incompatibility, from X to Y and either X or Y, to distinguish opposites from semantically similar words. Roth and Schulte im Walde (2014) proposed a method that combined patterns with discourse markers for classifying paradigmatic relations including antonymy, synonymy, and hypernymy. Recently, Schwartz et al (2015) used two prominent patterns from Lin et al (2003) to learn word embeddings that distinguished antonyms from similar words in determining degrees of similarity and word analogy.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network

Nguyen¹,

Walde²,

Vu³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntactic parse trees. In addition to the lexical and syntactic information, we successfully integrate the distance between the related words along the syntactic path as a new pattern feature. The results from classification experiments show that AntSyn-NET improves the performance over prior pattern-based methods.

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Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification

Cited by 41 publications

References 30 publications

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

A Neural Network Model for Efficient Antonymy-Synonymy Classification by Exploiting Co-occurrence Contexts and Word-Structure Patterns

Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network

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