In the absence of labeled training data, exploring alternative unsupervised techniques beyond cosine similarity can offer valuable insights and improvements for the Semantic Textual Relatedness (STR) task. Some alternative approaches to consider include:
Word Embedding Alignment: Utilize techniques such as word embedding alignment to compare the semantic similarity between sentences. By aligning word embeddings from different sentences or documents, it is possible to measure the relatedness based on the alignment quality and similarity scores.
Topic Modeling: Apply topic modeling algorithms such as Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) to identify latent topics in text data. By comparing the topic distributions of sentences, one can infer their semantic relatedness and thematic similarities.
Graph-based Methods: Explore graph-based methods like TextRank or Word Mover's Distance (WMD) to represent sentences as nodes in a graph and measure their relatedness based on graph connectivity or distance metrics. These methods can capture semantic relationships beyond simple word overlap.
Siamese Networks: Implement Siamese neural networks to learn sentence embeddings that capture semantic similarity. By training the network on pairs of sentences with known relatedness labels, it can learn to encode the semantic content of sentences and compute similarity scores.
BERT-based Sentence Transformers: Utilize pre-trained BERT models fine-tuned on unsupervised tasks to generate sentence embeddings. By comparing the embeddings of sentences using similarity metrics like cosine similarity or Euclidean distance, one can assess their semantic relatedness.
By exploring these unsupervised techniques in conjunction with cosine similarity, researchers can enhance the STR task's performance, especially in scenarios where labeled training data is limited or unavailable.