get_synonyms
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)
get_synonyms has 41 facts recorded in Dontopedia across 5 references, with 6 live disagreements.
Mostly:rdf:type(5), has parameter(3), returns(3)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (13)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
usedByUsed by(4)
- Bert Model
ex:bert-model - Bert Tokenizer
ex:bert-tokenizer - Redis Client
ex:redis-client - Word Token
ex:word-token
demonstratesDemonstrates(2)
- Code Snippet
ex:code-snippet - Example Usage
ex:example-usage
assignedByAssigned by(1)
- Synonyms Variable
ex:synonyms-variable
assignedFromAssigned From(1)
- Synonyms Variable
ex:synonyms-variable
consistsOfConsists of(1)
- Hybrid Approach
ex:hybrid-approach
createdByCreated by(1)
- Synonyms Set
ex:synonyms-set
definesFunctionDefines Function(1)
- Code Snippet
ex:code-snippet
implementedByImplemented by(1)
- Lexical Method
ex:lexical-method
relatesRelates(1)
- Function Relationship
ex:function-relationship
Other facts (40)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Python Function | [1] |
| Rdf:type | Function Call | [2] |
| Rdf:type | Function | [3] |
| Rdf:type | Function | [4] |
| Rdf:type | Python Function | [5] |
| Has Parameter | term | [2] |
| Has Parameter | term | [3] |
| Has Parameter | word | [5] |
| Returns | Synonyms Variable | [2] |
| Returns | Closest Synonyms | [3] |
| Returns | List of Synonyms | [5] |
| Calls Method | wordnet.synsets | [5] |
| Calls Method | syn.lemmas | [5] |
| Calls Method | lemma.name | [5] |
| Purpose | Find synonyms using WordNet | [4] |
| Purpose | retrieve-synonyms-for-word | [5] |
| Iteration Variable | syn | [5] |
| Iteration Variable | lemma | [5] |
| Has Parameter | term | [1] |
| Calls | Get Contextual Embeddings Function | [1] |
| Computes | Similarity Scores | [1] |
| Is Defined in | External Library | [2] |
| Implements | Synonym Expansion Task | [3] |
| Function Name | get_synonyms | [4] |
| Parameter | word | [4] |
| Return Type | list | [4] |
| Contains Loop | Wordnet Synsets Loop | [4] |
| Uses | Wordnet | [4] |
| Uses Data Structure | set | [5] |
| Initializes Variable | synonyms | [5] |
| Data Structure Type | set | [5] |
| Nested Loop | Lemmas Iteration | [5] |
| Outer Loop | Synsets Iteration | [5] |
| Adds to Collection | lemma.name | [5] |
| Converts to | list | [5] |
| Algorithm | synset-then-lemma-iteration | [5] |
| Returns Type | list | [5] |
| Uses Nested Loops | true | [5] |
| Outer Loop Target | wordnet.synsets | [5] |
| Inner Loop Target | syn.lemmas | [5] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (5)
ctx:claims/beam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfc- full textbeam-chunktext/plain1 KB
doc:beam/5d8a681b-1fe3-4aff-8534-8603ba9d9bfcShow excerpt
inputs = tokenizer(term, return_tensors='pt', padding=True, truncation=True) outputs = model(**inputs) embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings ``` ### Step 4: Retrieve Synonyms B…
ctx:claims/beam/a296a949-2c13-4366-96e2-0759ac1499ba- full textbeam-chunktext/plain995 B
doc:beam/a296a949-2c13-4366-96e2-0759ac1499baShow excerpt
return closest_synonyms # Test the synonym expansion terms = ["happy", "sad", "angry"] for term in terms: synonyms = get_synonyms(term) print(f"Synonyms for '{term}': {synonyms}") ``` ### Summary 1. **Setup Environment**: Ens…
ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7- full textbeam-chunktext/plain1 KB
doc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7Show excerpt
for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…
ctx:claims/beam/03e9535f-b129-47f6-9c40-934a5df3e95a- full textbeam-chunktext/plain1 KB
doc:beam/03e9535f-b129-47f6-9c40-934a5df3e95aShow excerpt
Here's an example of a hybrid approach that combines WordNet and context-aware embeddings: ```python from transformers import BertTokenizer, BertModel import torch import nltk from nltk.corpus import wordnet nltk.download('wordnet') toke…
ctx:claims/beam/524c612c-d2c8-4637-96e1-a8bf9b0b6122- full textbeam-chunktext/plain1 KB
doc:beam/524c612c-d2c8-4637-96e1-a8bf9b0b6122Show excerpt
- **Dataset Characteristics**: If your dataset has specific characteristics or domain-specific language, you might want to experiment with both models to see which performs better on your particular data. ### Conclusion For query reformula…
See also
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