Improving word coverage using unsupervised morphological analyser

Unsupervised Morphological Expansion of Small Datasets for Improving Word Embeddings

We present a language independent, unsupervised method for building word embeddings using morphological expansion of text. Our model handles the problem of data sparsity and yields improved word embeddings by relying on training word embeddings on artificially generated sentences. We evaluate our method using small sized training sets on eleven test sets for the word similarity task across seve...

Improving Subjectivity Detection using Unsupervised Subjectivity Word Sense Disambiguation

In this work, we present a sentence-level subjectivity detection method, which relies on Subjectivity Word Sense Disambiguation (SWSD). We use an unsupervised sense clustering-based method for SWSD. In our method, semantic resources tagged with emotions and sentiment polarities are used to apply subjectivity detection, intervening Word Sense Disambiguation sub-tasks. Through an experimental stu...

Experiments in Improving Unsupervised Word Sense Disambiguation

Unsupervised Morphological Analysis Using Tries

This article presents an unsupervised morphological analysis algorithm to segment words into roots and affixes. The algorithm relies on word occurrences in a given dataset. Target languages are English, Finnish, and Turkish, but the algorithm can be used to segment any word from any language given the wordlists acquired from a corpus consisting of words and word occurrences. In each iteration, ...

Hebrew Morphological Disambiguation: An Unsupervised Stochastic Word-based Approach

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