Unsupervised Query Segmentation Using Monolingual Word Alignment Method

Unsupervised Query Segmentation Using Monolingual Word Alignment Method

In this paper, we propose a novel unsupervised approach to query segmentation using the word alignment model which is usually adopted in statistical machine translation system. Query segmentation is to obtain complete phrases or concepts in a query by segmenting a sequence of query terms, which is an important query processing procedure for improving information retrieval performance in search ...

Collocation Extraction Using Monolingual Word Alignment Method

Statistical bilingual word alignment has been well studied in the context of machine translation. This paper adapts the bilingual word alignment algorithm to monolingual scenario to extract collocations from monolingual corpus. The monolingual corpus is first replicated to generate a parallel corpus, where each sentence pair consists of two identical sentences in the same language. Then the mon...

Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings

Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monoling...

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

Improving Word Alignment Models using Structured Monolingual Corpor