Fuzzy Bag-of-Words Model for Document Representation

Abstract

Fuzzy Bag-of-Words Model for Document Representation java project report One key issue in text mining and natural language processing is how to effectively represent documents using numerical vectors. One classical model is the Bag-of-Words (BoW). In a BoW-based vector representation of a document, each element denotes the normalized number of occurrence of a basis term in the document.

Fuzzy Bag-of-Words Model for Document Representation java project report To count the number of occurrence of a basis term, BoW conducts exact word matching, which can be regarded as a hard mapping from words to the basis term. BoW representation suffers from its intrinsic extreme sparsity, high dimensionality, and inability to capture high-level semantic meanings behind text data. To address the aforementioned issues, we propose a new document representation method named fuzzy Bag-of-Words (FBoW) in this paper.

System Configuration:

H/W System Configuration:-

System          : Pentium I3 Processor.
Hard Disk       : 500 GB.
Monitor          : Standard LED Monitor
Input Devices : Keyboard
Ram               : 4 GB

S/W System Configuration:-

Operating system              : Windows 7/8/10.
Available Coding Language : Java and Phonegap
Database                          : MYSQL

Conclusion

In Fuzzy Bag-of-Words Model for Document Representation work we combine word embeddings with classic BoW representations using fuzzy set theory. We show that max-pooled word vectors are a special case of FBoW, which implies that they should be compared via the fuzzy Jaccard index rather than the more standard cosine similarity. We also present a simple and novel algorithm, DynaMax, which corresponds to projecting word vectors onto a subspace dynamically generated by the given sentences before max-pooling over the features.

DynaMax outperforms averaged word vectors compared with cosine similarity on every benchmark STS task when word vectors are trained unsupervised. It even performs comparably to supervised vectors that directly optimise cosine similarity between paraphrases, despite being completely unrelated to that objective.