using linked disambiguated distributional networks for word sense disambiguation

Universität Hamburg, – Name of Presenter: Titel of Talk/Slideset (Version: 27.6.2014) – Slide 1

Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation

Chris [email protected]

Alexander [email protected]

Stefano [email protected]

Simone Paolo [email protected]

Dmitry [email protected]

Presented by:

Universität Hamburg – Panchenko et al.: Using Linked Disambiguated Distributional Networks for WSD (04.04.2017) – Slide 2

Contribution

An unsupervised knowledge-based approach to WSD based on the Hybrid Aligned Resource (HAR) by Faralli et al. (2016):

• Learning sparse distributional sense representations from text;• Linking them to the language resource (LR);• Expanding sense representations of the LR.

Combines distributional and knowledge-based sense representations.

Faralli S., Panchenko A., Biemann C., and Ponzetto S.P. (2016). Linked disambiguated distributional semantic networks. In International Semantic Web Conference (ISWC’2016), pages 56–64, Kobe, Japan. Springer.


Contribution

An unsupervised knowledge-based approach to WSD based on the Hybrid Aligned Resource (HAR) by Faralli et al. (2016):

• Learning sparse distributional sense representations from text;• Linking them to the language resource (LR);• Expanding sense representations of the LR.

Combines distributional and knowledge-based sense representations.

The method requires no linking of texts to a sense inventory and thus can be applied to large text collections.

Faralli S., Panchenko A., Biemann C., and Ponzetto S.P. (2016). Linked disambiguated distributional semantic networks. In International Semantic Web Conference (ISWC’2016), pages 56–64, Kobe, Japan. Springer.


Linked Disambiguated Distributional Networks for WSD

Distributional corpus-derived information

- Hybrid Aligned Resource (HAR) by Faralli et al. (2016)- Distributional sense representations linked to a lexical resource (WordNet, ...)

- Sample entries of the HAR for the words “mouse” and “keyboard”.



- Hybrid Aligned Resource (HAR) by Faralli et al. (2016)- Distributional sense representations linked to a lexical resource (WordNet, ...)

- Sample entries of the HAR for the words “mouse” and “keyboard”.

Information from the knowledge base


Construction of the Hybrid Aligned Resource (HAR):1. Building a Distributional Thesaurus (DT).2. Word Sense Induction.3. Labeling Word Senses with Hypernyms.4. Disambiguation of Related Terms and Hypernyms.5. Retrieval of Context Clues.



Construction of the Hybrid Aligned Resource (HAR):1. Building a Distributional Thesaurus (DT).2. Word Sense Induction.3. Labeling Word Senses with Hypernyms.4. Disambiguation of Related Terms and Hypernyms.5. Retrieval of Context Clues.

HAR Datasets used in our experiment (Faralli et al., 2016):

− news:• a 100 million sentence news corpus• average polysemy of 2.3

− wiki: • a 35 million sentence Wikipedia corpus• average polysemy of 1.8



Using the Hybrid Aligned Resource in Word Sense Disambiguation:− WordNet: this baseline model relies solely on the WordNet:

• Synonyms• Glosses• Target synset + synsets directly connected to it

− WordNet + Related: augments the WordNet-based representation with related terms from the corpus-induced word senses.

− WordNet + Related + Context: all features of the previous model plus context clues obtained by aggregating features of the sense cluster words.




- The third sense of the word “disk” in the WordNet:- The initial WordNet-based sense representation vs- The enriched via linking to HAR sense representation

- Enriched with related words from the HAR


Evaluation: Research Questions

RQ 1:

Does the enriched sense representation improve WSD performance compared to the original WordNet-based representations?


Evaluation: Research Questions

RQ 1:

Does the linked sense representation improve WSD performance compared to the original WordNet-based sense representation?

RQ 2:

What is the quality of our approach compared to the SOTA unsupervised knowledge-based WSD systems?


Evaluation: Dataset and Evaluation Metrics

SemEval-2007 Task 16 “Evaluation of wide-coverage knowledge resources” (Cuadros and Rigau, 2007):

- specifically designed for evaluating the impact of lexical resources on the WSD performance


Evaluation: Dataset and Evaluation Metrics

SemEval-2007 Task 16 “Evaluation of wide-coverage knowledge resources” (Cuadros and Rigau, 2007):

- specifically designed for evaluating the impact of lexical resources on the WSD performance

- the task dataset is based on the WordNet-labeled sentences from:- Senseval-3 (Mihalcea et al., 2004)- SemEval-2007 Task 17 (Pradhan et al., 2007)


RQ1: Results



RQ1: Dataset and Evaluation Metrics



RQ2: Baselines


The state of the art unsupervised knowledge-based methods: - WN+XWN (Cuadros and Rigau, 2007)

- WordNet + eXtend WordNet (parsing WordNet glosses)- KnowNet (Cuadros and Rigau, 2008)

- based on snippets retrieved with a web search engine- BabelNet (Navigli and Ponzetto, 2012)

- Wikipedia articles + WordNet synsets- NASARI (Camacho-Collados et al., 2015):

- vector representations of senses based on Wikipedia and WordNet- lexical or sense-based feature spaces- The links between WordNet and Wikipedia are retrieved from BabelNet


RQ2: Results



Conclusions

− We presented a novel approach to knowledge-based WSD:• Learning sparse distributional sense representations from text;• Linking them to the language resource (LR);• Expanding sense representations of the LR.

− Possibility to use large corpora: not limited to Wikipedia-linked texts as in BabelNet, NASARI.


Conclusions

− We presented a novel approach to knowledge-based WSD:• Learning sparse distributional sense representations from text;• Linking them to the language resource (LR);• Expanding sense representations of the LR.

− A possibility to use large corpora: the method is not limited to Wikipedia-linked texts as in BabelNet, NASARI.

− RQ1: Distributional sense representations let us substantially outperform the model based solely on the lexical resource.

− RQ2: Comparable performance to the state-of-the-art hybrid approaches leveraging corpus-based features.

Universität Hamburg, – Name of Presenter: Titel of Talk/Slideset (Version: 27.6.2014) – Slide 21

We acknowledge the support of:


References

[1] Chris Biemann and Martin Riedl. 2013. Text: Now in 2D! A Framework for Lexical Expansion with Contextual Similarity. Journal of Language Modelling, 1(1):55–95

[2] Chris Biemann. 2006. Chinese whispers - an efficient graph clustering algorithm and its application to natural language processing problems. In Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing, pages 73–80, New York City. Association for Computational Linguistics.

[3] Jose Camacho-Collados, Mohammad Taher Pilehvar, ´ and Roberto Navigli. 2015a. Nasari: a novel approach to a semantically-aware representation of items. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 567–577, Denver, Colorado. Association for Computational Linguistics.

[4] Montse Cuadros and German Rigau. 2007. Semeval- 2007 task 16: Evaluation of wide coverage knowledge resources. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), pages 81–86, Prague, Czech Republic. Association for Computational Linguistics.

[5] Montse Cuadros and German Rigau. 2008. KnowNet: Building a large net of knowledge from the web. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 161–168, Manchester, UK, August. Coling 2008 Organizing Committee.

[6] Stefano Faralli, Alexander Panchenko, Chris Biemann, and Simone P. Ponzetto. 2016. Linked disambiguated distributional semantic networks. In International Semantic Web Conference (ISWC’2016), pages 56–64, Kobe, Japan. Springer.

[7] Roberto Navigli and Simone Paolo Ponzetto. 2012. Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 193:217– 250.

using linked disambiguated distributional networks for word sense disambiguation

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