Synonyms

# Extract synonyms for each token synonyms = [] for token in tokens: # Use WordNet to get synonyms synsets = nltk.corpus.wordnet.synsets(token) for synset in synsets: for lemma in synset.lemma…

nlp = spacy.load("en_core_web_sm") lemmatizer = WordNetLemmatizer() def get_wordnet_pos(treebank_tag): """Converts treebank POS tags to WordNet POS tags.""" if treebank_tag.startswith('J'): return wordnet.ADJ elif treeb…

- **Input Queue**: Kafka queue to receive raw queries. - **Tokenization**: Stage for tokenizing the queries. - **Entity Recognition**: Stage for recognizing entities in the queries. - **Synonym Expansion**: Stage for expanding s…

results_db = PostgreSQL("Results") # Define the message queues kafka_queue = Kafka("Kafka Queue") # Define the data flows tokenization >> Edge(label="Tokens") >> kafka_queue kafka_queue >> Edge(label="Token…

- Entities are passed from `Entity Recognition` to `Synonym Expansion`. - Synonyms are passed from `Synonym Expansion` to `Rewriting`. - Rewritten queries are passed from `Rewriting` to `Filtering`. - Filtered results are passed…

When you call the `expand_synonyms` function, it will attempt to fetch the synonyms and retry if it encounters a 503 status code or network errors. ### Conclusion By implementing a retry mechanism with exponential backoff and handling spe…

3. **Caching**: Use a caching layer to reduce the load on the underlying data store. 4. **Load Balancing**: Distribute the load across multiple instances of the module. 5. **Fault Tolerance**: Implement retry mechanisms and fallback strateg…

2. **Efficient Data Structures**: Use a more efficient data structure like a `defaultdict` to handle multiple synonyms. 3. **Integration with Elasticsearch**: Ensure that the rewritten queries are indexed correctly. ### Updated Code Here'…

'term': 'hi' } } }) print(response['hits']['total']['value']) # Output: 1 ``` ### Explanation 1. **Thread Safety**: - Use a `threading.Lock` to ensure thread safety when adding and retrieving synonyms. 2. **E…

[Turn 10116] User: hmm, how do I handle synonyms that have multiple meanings in different contexts? [Turn 10117] Assistant: Handling synonyms that have multiple meanings in different contexts can be challenging, but there are several strat…

# Determine context and retrieve synonyms query = "I want to visit the bank of the river." context = module.determine_context(query) print(module.get_synonyms('bank', context)) # Output: ['river bank'] ``` ### 3. Hierarchical Synonym Stru…

[Turn 10120] User: I'm trying to improve the performance of my query rewriting system by optimizing the synonym lookup module. I've been exploring different data structures and algorithms, but I'm unsure which one would be the most suitable…

print(module.get_synonyms('hello')) # Output: [] ``` ### Explanation 1. **Use `defaultdict`**: - `defaultdict(list)` allows storing multiple synonyms for a single term. - This ensures that each term can have a list of synonyms. 2.…

First, ensure that your Elasticsearch index is correctly configured with the synonym analyzer and filter. Your current configuration looks mostly correct, but there are a few improvements and checks we can make. ### 2. Use `synonyms_path` …

Benchmarking involves measuring the performance of your system under various conditions to identify bottlenecks and areas for improvement. #### Steps: 1. **Generate Test Data**: - Create a large set of test data that includes terms and…

``` I'm looking for ways to improve the accuracy of the expansion algorithm. Can someone help me with some suggestions? ->-> 2,20 [Turn 10147] Assistant: To improve the accuracy of your synonym expansion algorithm, you can consider several…

logging.basicConfig(level=logging.INFO) def thesaurus_lookup(word): start_time = time.time() # Simulate the lookup time.sleep(0.1) end_time = time.time() logging.info(f"Lookup took {end_time - start_time} seconds") …

term_embedding = get_contextual_embeddings(term) closest_synonyms = [] for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_context…

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…

- **Reason**: This involves setting up and configuring a caching layer (e.g., Redis) to store and retrieve contextual embeddings and synonyms efficiently. It may also require tuning the cache settings and handling cache invalidation. 4.…

- **Time-Consuming Aspects**: - Model selection and configuration. - Integration with existing systems. - Performance tuning and optimization. 2. **Integrate with Existing Thesaurus** - **Steps**: - Map the output…

synonym_embedding = synonym_outputs.last_hidden_state[0][0] # [CLS] token embedding similarity = torch.dot(word_embedding, synonym_embedding).item() if similarity > best_similarity: best_similar…

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased') def get_context_aware_synonyms(word, context_sentence): inputs = tokenizer(context_sentence, return_tensors='pt', pad…

Can you suggest a better way to handle synonym expansion and improve my detection accuracy? ->-> 4,2 [Turn 10387] Assistant: Handling synonym mismatches is indeed a challenging aspect of natural language processing, and while WordNet is a …