---
title: "Morpho-syntactic Lexicon Generation Using Graph-based"
description: "They are expensive to build by hand, exist for few languages, and are usually small. 77 references, cited by 14 papers here."
authors: ["Manaal Faruqui", "Ryan McDonald", "Radu Soricut"]
year: 2016
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/Q16-1001/"
doi: "10.1162/tacl_a_00079"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/faruqui-2016-morpho-syntactic-lexicon-generation-graph/"
---
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# Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning

**Authors:** [Manaal Faruqui](https://md.research.elamur.ai/free/en/authors/manaal-faruqui/), [Ryan McDonald](https://md.research.elamur.ai/free/en/authors/ryan-mcdonald/) and [Radu Soricut](https://md.research.elamur.ai/free/en/authors/radu-soricut/) · **Year:** 2016 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2016](https://md.research.elamur.ai/free/en/venues/tacl/2016/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/Q16-1001/) · [PDF](https://aclanthology.org/Q16-1001.pdf) · [DOI 10.1162/tacl\_a\_00079](https://doi.org/10.1162/tacl_a_00079) · [OpenAlex](https://openalex.org/W2211744643)

**Citations:** 17 citations (OpenAlex, 2026-10-14) · [14](https://md.research.elamur.ai/free/en/papers/faruqui-2016-morpho-syntactic-lexicon-generation-graph/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Morpho-syntactic lexicons record which grammatical attributes (part of speech, tense, number, etc.) each word can carry. They are expensive to build by hand, exist for few languages, and are usually small. Faruqui, McDonald and Soricut treat lexicon creation as graph-based semi-supervised learning. Word types are nodes, and a small annotated seed lexicon labels some of them. Edges link words that share an unsupervised word cluster, a short prefix or suffix, or a morphological transformation found by the Soricut and Och (2015) tool. The key idea is that edges carry features with weights learned separately for each attribute. An edge can therefore pass a value along or reverse it, for example when a suffix change signals a different tense. Classic label propagation, by contrast, assumes connected nodes are similar. All signals come from raw news text, so the method works for any language. From roughly 1,000 seed words, the authors grow lexicons over 100 times larger for 11 languages, checked against UD treebank data or human-made lexicons. Used as features, these lexicons improve morphological tagging and dependency parsing, offering a cheap path to such resources for low-resource languages.

## Key points
- A seed of about 1,000 words is expanded more than 100-fold with high quality for 11 languages of differing morphological complexity.
- As extra features, the lexicons cut errors by an average of 15.4% in morphological tagging and 5.3% in dependency parsing across the 11 languages, on publicly available treebanks.
- The graph has three edge types:
  - Exchange clustering of the 1M most frequent words from a year of news into 256 clusters.
  - 2- and 3-character prefixes and suffixes seen at least twice in the seed lexicon.
  - Morphological transformations such as suffix:ed:ing.
  - To keep the graph sparse, each node gets at most 100 random neighbours per feature.
- Each attribute value is estimated as a tanh of feature-weighted neighbour values, which resembles a naive mean-field approximation of the Ising model.
  - Weights are first learned with supervision, then propagated with the weights fixed.
  - Paradigm projection then maps each word to the closest valid attribute combination; attributes are handled independently via binary relevance.
- The features suit concatenative morphology and may perform poorly for languages like Arabic or Hebrew. Proposed future work includes joint learning and propagation, and richer features such as word embeddings and Wiktionary relations.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Manaal Faruqui, Ryan McDonald and Radu Soricut. 2016. Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{faruqui2016morpho,
  title = {{Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning}},
  author = {Manaal Faruqui, Ryan McDonald and Radu Soricut},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2016},
  doi = {10.1162/tacl\_a\_00079},
  url = {https://doi.org/10.1162/tacl_a_00079}
}
```

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*Updated 30 October 2026*