We study the task of automatically expanding WordNet-style lexical resources to new languages through sense generation. We generate senses by associating target-language lemmas with existing lexical concepts via semantic projection. Given a sense-tagged English corpus and its translation, our method projects the annotated synsets onto aligned target-language tokens and assigns the corresponding lemmas to those synsets. To generate alignments and ensure their quality, we augment a pretrained base aligner with a bilingual dictionary, which is also used to filter incorrect sense projections. We evaluate the method on multiple languages, comparing it to prior methods, as well as dictionary-based and large language model baselines. Results show that the proposed project-and-filter strategy improves precision while remaining interpretable and resource-efficient. We release our code, documentation, and generated sense inventories at https://github.com/UAlberta-NLP/ExpandNet.
Figures & tables
French (dev)
Spanish
Chinese
Urdu
Method
Prec.
Cov.
HM
Prec.
Cov.
HM
Prec.
Cov.
HM
Prec.
Cov.
HM
UWN
46.1
56.9
50.9
71.9
51.4
60.0
45.0
5.8
10.3
59.9
13.1
21.5
Dict
5.2
93.7
9.9
8.5
88.2
15.5
7.8
75.1
14.1
9.4
44.0
21.5
OC14
71.1
8.2
14.6
72.0
9.9
17.4
34.3
2.8
5.2
44.0
0.5
1.0
M23
52.3
67.2
59.0
55.3
67.8
60.9
34.7
48.4
40.6
6.3
24.4
10.0
ChatGPT
44.3
44.3
44.3
55.3
55.3
55.3
41.8
41.8
41.8
50.5
50.5
50.5
Table 1. Results (in % ) on the SC20 corpus, showing the Sense Precision, Synset Coverage, and Harmonic Mean (HM) of the two.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Role
Content
System
You are a bilingual lexicon expert.
Given a dictionary definition, produce the single word in {TARGET_LANGUAGE} that best matches this definition.
Provide only the {TARGET_LANGUAGE} word without explanations.
User
{DICTIONARY_DEFINITION}
Appendix
Figure B1 . Prompt template for our LLM baselines.
Role
Content
System
You are an expert translator.
Translate from {SOURCE_LANGUAGE} to {TARGET_LANGUAGE}.
Provide only the translation without explanations.
User
{SOURCE_TEXT}
Appendix
Figure B2 . Prompt template for our translations.
n
NOUN PROPN PRON NUM NN PN PP GR PD D CA RP KP AP REP
v
VERB AUX VB AA TA KER MUL
a
ADJ AKP Q OR AD KD
r
ADV ADP PART SCONJ NEG P SE QW WALA SC I FR A
x
CCONJ INTJ SYM PUNCT DET X SPACE CC SM G U X PM EXP INT
Appendix
Table C1 . Our mapping of POS tags from multiple tagsets to the basic POS categories. All tags mapped to x are considered to denote function words and are excluded from projection.
Aligner
Dict.
POS
Prec.
Cov.
HM
DBAlign
Used
Used
82.8
66.6
73.8
DBAlign
Used
Off
80.8
68.3
74.0
SimAlign
Used
Used
84.1
62.2
71.5
SimAlign
Used
Off
83.9
59.5
69.6
SimAlign
Off
Off
49.1
68.1
57.1
Appendix
Table D1 . Results for variants of ExpandNet on SC20 , with Spanish as the target language. The Sense Precision, Synset Coverage, and their Harmonic Mean (HM) are reported for each method (in % ). “Dict.” indicates whether a dictionary-based filter was used. Similarly, “POS” indicates whether the POS filter was used.
Computer Science, Faculty of Computers and Information, Giza, Egypt · EECE, Faculty of Engineering, Giza, Egypt · Arabic Language and Literatures, Faculty of Arts, Giza, Egypt