WordNet

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for class…

- **Synonym Expansion**: Using WordNet for synonym expansion is a good start, but you can improve it by filtering out irrelevant synonyms and handling multi-word expressions. ### 2. **Handling Multi-Word Expressions** - Multi-word ex…

# 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…

- **Contextual Relevance**: Consider using a context-aware approach to filter synonyms based on the context of the query. - **Dependency Parsing**: Use dependency parsing to better understand the relationships between words in the query. #…

- **Continuous Monitoring**: Continuously monitor the performance of your pipeline after integration. - **Adjust Parameters**: Tune parameters such as cache size, batch size, and worker thread counts based on observed performance. ##…

- **Combine Truncation and Filtering**: Apply both truncation and filtering techniques to ensure the expanded query remains concise and relevant. ### Example Implementation Here's an example implementation that incorporates these strat…

elif term.endswith("ed"): return [term[:-2] + "ing"] # WordNet approach synonyms = set() for syn in wn.synsets(term): for lemma in syn.lemmas(): synonyms.add(lemma.name()) # NLP appr…

best_synonym = synonym return best_synonym word = 'happy' context_sentence = 'She felt happy after receiving the gift.' best_synonym = get_context_aware_synonyms(word, context_sentence) print(best_synonym) ``` ### 3. …

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…

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…

To improve query rewriting accuracy, you can integrate synonym expansion using spaCy and a thesaurus like WordNet. ```python from nltk.corpus import wordnet def get_synonyms(word): synonyms = set() for syn in wordnet.synsets(word)…

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 …