Pattern-Based Distinction of Paradigmatic Relations for German Nouns, Verbs, Adjectives

Walde, Sabine Schulte im; Köper, Maximilian

doi:10.1007/978-3-642-40722-2_19

Cited by 33 publications

(9 citation statements)

References 29 publications

(51 reference statements)

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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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“…The main idea is that if two words X and Y appear in one of these patterns, they are unlikely to represent synonymous pair. Schulte im Walde and Köper (2013) proposed a method to distinguish between the paradigmatic relations antonymy, synonymy and hypernymy in German, based on automatically acquired word patterns. Roth and Schulte im Walde (2014) combined general lexico-syntactic patterns with discourse markers as indicators for the same relations, both for German and for English.…”

Section: Related Workmentioning

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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“…Whereas the above mentioned approaches rely on additional knowledge sources, Turney (2006) developed a corpusbased approach to model relational similarity, addressing (among other tasks) the distinction between synonyms and antonyms. More recently, Schulte im Walde and Köper (2013) proposed to distinguish between the three relations antonymy, synonymy and hyponymy based on automatically acquired word patterns.…”

Section: Related Workmentioning

confidence: 99%

Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification

Roth¹,

Walde²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Distinguishing between paradigmatic relations such as synonymy, antonymy and hypernymy is an important prerequisite in a range of NLP applications. In this paper, we explore discourse relations as an alternative set of features to lexico-syntactic patterns. We demonstrate that statistics over discourse relations, collected via explicit discourse markers as proxies, can be utilized as salient indicators for paradigmatic relations in multiple languages, outperforming patterns in terms of recall and F 1 -score. In addition, we observe that markers and patterns provide complementary information, leading to significant classification improvements when applied in combination.

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Pattern-Based Distinction of Paradigmatic Relations for German Nouns, Verbs, Adjectives

Cited by 33 publications

References 29 publications

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction

Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network

Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification

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