scholarly journals Words Stemming Based on Structural and Semantic Similarity

2014 ◽  
Vol 3 (2) ◽  
pp. 89-99 ◽  
Author(s):  
Mohammad Hassan Dianati ◽  
Mohammad Hadi Sadreddini ◽  
Amir Hossein Rasekh ◽  
Seyed Mostafa Fakhrahmad ◽  
Hossein Taghi-Zadeh
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
A Performance

Words  stemming  is  one  of  the  important  issues  in  the field  of  natural  language processing  and  information retrieval.  There  are  different  methods  for stemming which are mostly language-dependent. Therefore, these  stemmers are only applicable  to  particular  languages.  Because  of the importance  of  this issue,  in  this paper, the proposed method for stemming is aimed to be language-independent. In the  proposed  stemmer,  a  bilingual  dictionary  is  used and  all  of  the  words  in  the dictionary are firstly clustered. The words’ clustering is based on their structural and semantic similarity. Finally, finding the stem of new coming words is performed by making use of the previously formatted clusters. To evaluate the proposed scheme, words  stemming is  done on both  Persian  and  English  languages.  The encouraging results  indicate  the  good  performance  of  the proposed  method  compared  with  its counterparts.

LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

2021 ◽  
pp. 2150006
Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Gold Standard ◽  
Question Answering ◽  
Knowledge Based ◽  
Benchmark Datasets ◽  
Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

Lexical Co-Occurrence and Contextual Window-Based Approach With Semantic Similarity for Query Expansion

2018 ◽  
pp. 1552-1575
Author(s):  
Jagendra Singh ◽  
Rakesh Kumar
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Efficient Method ◽  
Query Expansion ◽  
Ad Hoc ◽  
Hybrid Approach ◽  
Information Retrieval System ◽  
Optimal Combination ◽  
Benchmark Datasets ◽  
Pseudo Feedback

Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. Next, we explore semantic similarity approach based on word2vec for ranking the QE terms obtained from top pseudo-feedback documents. Further, we combine co-occurrence statistics, contextual window statistics, and semantic similarity based approaches together to select the best expansion terms for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed experimental results show significant improvement over baseline method.

Information Retrieval by Semantic Similarity

2006 ◽  
Vol 2 (3) ◽  
pp. 55-73 ◽  
Author(s):  
Angelos Hliaoutakis ◽  
Giannis Varelas ◽  
Epimenidis Voutsakis ◽  
Euripides G.M. Petrakis ◽  
Evangelos Milios
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity

Study on Application of Domain Ontology in Semantic Information Retrieval

2013 ◽  
Vol 433-435 ◽  
pp. 1662-1665
Author(s):  
Huan Hai Yang ◽  
Ming Yu Sun
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Calculation Method ◽  
Semantic Information ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Retrieval Method ◽  
Similarity Calculation ◽  
Semantic Information Retrieval

Considering weakness of the traditional retrieval method based on keyword matching, the paper introduced semantic into information retrieval, and proposed a semantic retrieval model based on ontology. The paper offered a construction method of domain ontology and implemented semantic reasoning using Jena and improved a semantic similarity calculation method.

scholarly journals A Semantic Framework for Evaluating Topical Search Methods

2011 ◽  
Vol 14 (1) ◽  
Author(s):  
Rocío L. Cecchini ◽  
Carlos M. Lorenzetti ◽  
Ana G. Maguitman ◽  
Filippo Menczer
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Evaluation Framework ◽  
Evaluation Metrics ◽  
Digital Information ◽  
Actual Performance ◽  
Semantic Framework ◽  
Similarity Data ◽  
Retrieval Systems ◽  
Information Retrieval Systems

The absence of reliable and efficient techniques to evaluate information retrieval systems has become a bottleneck in the development of novel retrieval methods. In traditional approaches users or hired evaluators provide manual assessments of relevance. However these approaches are neither efficient nor reliable since they do not scale with the complexity and heterogeneity of available digital information. Automatic approaches, on the other hand, could be efficient but disregard semantic data, which is usually important to assess the actual performance of the evaluated methods. This article proposes to use topic ontologies and semantic similarity data derived from these ontologies to implement an automatic semantic evaluation framework for information retrieval systems. The use of semantic simi- larity data allows to capture the notion of partial relevance, generalizing traditional evaluation metrics, and giving rise to novel performance measures such as semantic precision and semantic harmonic mean. The validity of the approach is supported by user studies and the application of the proposed framework is illustrated with the evaluation of topical retrieval systems. The evaluated systems include a baseline, a supervised version of the Bo1 query refinement method and two multi-objective evolutionary algorithms for context-based retrieval. Finally, we discuss the advantages of ap- plying evaluation metrics that account for semantic similarity data and partial relevance over existing metrics based on the notion of total relevance.

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