[Paper Review] Survey on the Use of Typological Information in Natural Language Processing

Helen O’Horan, Yevgeni Berzak+3 more|arXiv (Cornell University)|Oct 11, 2016

Natural Language Processing Techniques71 references33 citations

TL;DR

This paper provides a comprehensive survey of how linguistic typology—systematic classification of languages by structural and functional features—supports multilingual natural language processing (NLP). It reviews major typological databases, analyzes how typological information enhances multilingual NLP through transfer learning, joint modeling, and representation learning, and advocates for deeper integration of typological knowledge into NLP models to improve cross-lingual generalization and resource-poor language performance.

ABSTRACT

In recent years linguistic typology, which classifies the world's languages according to their functional and structural properties, has been widely used to support multilingual NLP. While the growing importance of typological information in supporting multilingual tasks has been recognised, no systematic survey of existing typological resources and their use in NLP has been published. This paper provides such a survey as well as discussion which we hope will both inform and inspire future work in the area.

Motivation & Objective

• To systematically survey existing typological resources and their applications in multilingual NLP, addressing a gap in prior literature.
• To examine how typological features—especially morphosyntactic and phonological—support cross-lingual transfer and multilingual modeling.
• To explore the potential of integrating typological knowledge into neural and structured prediction models for improved generalization.
• To investigate how NLP techniques can support the automatic construction and expansion of typological databases.
• To inspire future research by identifying underexplored avenues for leveraging linguistic universals and variation in NLP systems.

Proposed method

• Surveying major typological databases: WALS, SSWL, APiCS, PHOIBLE, LAPSyD, and URIEL, assessing their coverage, structure, and utility for NLP.
• Categorizing NLP applications of typological information into explicit (e.g., feature-based constraints) and implicit (e.g., in multilingual embeddings) integration.
• Reviewing modeling frameworks such as posterior regularization, generalized expectation, and dual decomposition for incorporating soft typological constraints into inference.
• Analyzing multilingual word embedding approaches that align representations across languages and how typological features can guide or improve such alignment.
• Evaluating recent work on mapping word embeddings to interpretable typological representations to enable knowledge injection into neural models.
• Proposing that NLP can assist in automating typological data collection, reducing reliance on manual curation and expanding coverage.

Experimental results

Research questions

• RQ1 How are existing typological databases structured, and what is their coverage and reliability for NLP applications?
• RQ2 In what ways can typological information be explicitly or implicitly integrated into multilingual NLP models to improve performance?
• RQ3 To what extent can NLP techniques support the automatic extraction and expansion of typological knowledge from linguistic corpora?
• RQ4 How do typological features enhance cross-lingual transfer, joint learning, and representation learning in multilingual NLP?
• RQ5 What are the most effective modeling frameworks for incorporating typological constraints into NLP inference and training?

Key findings

• Typological databases such as WALS, SSWL, and URIEL provide structured, empirically grounded features across thousands of languages, enabling cross-linguistic comparison.
• Explicit integration of typological constraints via methods like posterior regularization and generalized expectation improves performance in POS tagging, parsing, and information extraction.
• Multilingual word embeddings benefit from typological priors, with studies showing improved alignment between word representations and semantic meaning across languages.
• Recent work demonstrates that word embeddings can be mapped to interpretable typological features, enabling knowledge injection into neural models.
• NLP techniques show promise in automating typological data collection, potentially reducing manual curation and expanding coverage of under-resourced languages.
• The integration of typological knowledge into multilingual NLP models leads to better generalization, especially in low-resource settings, by leveraging linguistic universals and structural patterns.