Integrating Semantic Knowledge into Text Similarity and Information Retrieval

2007 ◽  
Author(s):  
Christof Muller ◽  
Iryna Gurevych ◽  
Max Muhlhauser
Keyword(s):  
Information Retrieval ◽  
Semantic Knowledge ◽  
Text Similarity

CLOSING THE VOCABULARY GAP FOR COMPUTING TEXT SIMILARITY AND INFORMATION RETRIEVAL

2008 ◽  
Vol 02 (02) ◽  
pp. 253-272
Author(s):  
CHRISTOF MÜLLER ◽  
IRYNA GUREVYCH ◽  
MAX MÜHLHÄUSER
Keyword(s):  
Information Retrieval ◽  
Career Guidance ◽  
Ad Hoc ◽  
Semantic Relatedness ◽  
Boolean Model ◽  
Semantic Knowledge ◽  
Text Similarity ◽  
Retrieval Task ◽  
Lexical Semantic ◽  
Lexical Semantic Knowledge

This paper studies the integration of lexical semantic knowledge in two related semantic computing tasks: ad-hoc information retrieval and computing text similarity. For this purpose, we compare the performance of two algorithms: (i) using semantic relatedness, and (ii) using a conventional extended Boolean model [13] with additional query expansion. For the evaluation, we use two different test collections in the German language especially suitable to study the vocabulary gap problem: (i) GIRT [5] for the information retrieval task, and (ii) a collection of descriptions of professions built to evaluate a system for electronic career guidance in the information retrieval and text similarity tasks. We found that integrating lexical semantic knowledge increases the performance for both tasks. On the GIRT corpus, the performance is improved only for short queries. The performance on the collection of professional descriptions is improved, but crucially depends on the accurate preprocessing of the natural language essays employed as topics.

scholarly journals Multilingual Legal Information Retrieval System for Mapping Recitals and Normative Provisions

2020 ◽  
Author(s):  
Rohan Nanda ◽  
Llio Humphreys ◽  
Lorenzo Grossio ◽  
Adebayo Kolawole John
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Text Similarity ◽  
Legal Information ◽  
Additional Information ◽  
Eu Legislation ◽  
Multilingual Corpus ◽  
Eu Directives ◽  
Develop State

This paper presents a multilingual legal information retrieval system for mapping recitals to articles in European Union (EU) directives and normative provisions in national legislation. Such a system could be useful for purposive interpretation of norms. A previous work on mapping recitals and normative provisions was limited to EU legislation in English and only one lexical text similarity technique. In this paper, we develop state-of-the-art text similarity models to investigate the interplay between directive recitals, directive (sub-)articles and provisions of national implementing measures (NIMs) on a multilingual corpus (from Ireland, Italy and Luxembourg). Our results indicate that directive recitals do not have a direct influence on NIM provisions, but they sometimes contain additional information that is not present in the transposed directive sub-article, and can therefore facilitate purposive interpretation.

Enhancing Web Search through Query Expansion

2011 ◽  
pp. 752-757 ◽  
Author(s):  
Daniel Crabtree
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
Query Expansion ◽  
Web Search ◽  
User Involvement ◽  
Semantic Knowledge ◽  
Web Pages ◽  
Search Performance ◽  
Interactive Query ◽  
Web Search Engines

Web search engines help users find relevant web pages by returning a result set containing the pages that best match the user’s query. When the identified pages have low relevance, the query must be refined to capture the search goal more effectively. However, finding appropriate refinement terms is difficult and time consuming for users, so researchers developed query expansion approaches to identify refinement terms automatically. There are two broad approaches to query expansion, automatic query expansion (AQE) and interactive query expansion (IQE) (Ruthven et al., 2003). AQE has no user involvement, which is simpler for the user, but limits its performance. IQE has user involvement, which is more complex for the user, but means it can tackle more problems such as ambiguous queries. Searches fail by finding too many irrelevant pages (low precision) or by finding too few relevant pages (low recall). AQE has a long history in the field of information retrieval, where the focus has been on improving recall (Velez et al., 1997). Unfortunately, AQE often decreased precision as the terms used to expand a query often changed the query’s meaning (Croft and Harper (1979) identified this effect and named it query drift). The problem is that users typically consider just the first few results (Jansen et al., 2005), which makes precision vital to web search performance. In contrast, IQE has historically balanced precision and recall, leading to an earlier uptake within web search. However, like AQE, the precision of IQE approaches needs improvement. Most recently, approaches have started to improve precision by incorporating semantic knowledge.

A NEW APPROACH FOR TEXT SIMILARITY USING ARTICLES

2008 ◽  
Vol 07 (01) ◽  
pp. 23-34 ◽  
Author(s):  
ELSAYED ATLAM
Keyword(s):  
Information Retrieval ◽  
Text Analysis ◽  
Traditional Method ◽  
Text Similarity ◽  
Traditional Methods ◽  
Document Similarity ◽  
New Approach ◽  
Text Collections ◽  
Subject Areas

Conventional approaches to text analysis and information retrieval which measured document similarity by considering all information in texts are relatively inefficiency for processing large text collections in heterogeneous subject areas. Previous researches showed that evidence from passage can improve retrieval results. But it also raised questions about how passage is defined, how they can be ranked efficiently, and what is their proper rule in long structure documents. Moreover, the frequency of "the" with important sentence is efficiently to summarize the text by dexterity way. We previously proposed an approach for extracting sentences which including article "the" by some restrict rules to carry out effectiveness passages. Based on previous approaches, this paper presents a new Passage SIMilarity (P-SIM) measurements between documents based on effectiveness passages after extracting them using article "the". Moreover, our new approach showing that this method is more efficient than traditional methods. Also, Recall and Precision are achieved by 92.6% and 97.5% respectively, depending on extracted passages. Furthermore, Recall and Precision significantly improved by 38.3% and 44.2% over the traditional method. The proposed methods are applied to 3,990 articles from the large tagged corpus.

scholarly journals Math-word embedding in math search and semantic extraction

2020 ◽  
Vol 125 (3) ◽  
pp. 3017-3046 ◽  
Author(s):  
André Greiner-Petter ◽  
Abdou Youssef ◽  
Terry Ruas ◽  
Bruce R. Miller ◽  
Moritz Schubotz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Language Processing ◽  
Digital Library ◽  
Question Answering ◽  
Semantic Knowledge ◽  
Word Embedding ◽  
Mathematical Functions ◽  
Search Tasks ◽  
Math Search

AbstractWord embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate descriptions, contributing to the relative dearth of machine learning applications for information retrieval in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in word embedding, it is worthwhile to explore their use and effectiveness in math information retrieval tasks, such as math language processing and semantic knowledge extraction. In this paper, we explore math embedding by testing it on several different scenarios, namely, (1) math-term similarity, (2) analogy, (3) numerical concept-modeling based on the centroid of the keywords that characterize a concept, (4) math search using query expansions, and (5) semantic extraction, i.e., extracting descriptive phrases for math expressions. Due to the lack of benchmarks, our investigations were performed using the arXiv collection of STEM documents and carefully selected illustrations on the Digital Library of Mathematical Functions (DLMF: NIST digital library of mathematical functions. Release 1.0.20 of 2018-09-1, 2018). Our results show that math embedding holds much promise for similarity, analogy, and search tasks. However, we also observed the need for more robust math embedding approaches. Moreover, we explore and discuss fundamental issues that we believe thwart the progress in mathematical information retrieval in the direction of machine learning.

Combining IR Models for Bengali Information Retrieval

2018 ◽  
Vol 8 (3) ◽  
pp. 68-83
Author(s):  
Soma Chatterjee ◽  
Kamal Sarkar
Keyword(s):  
Information Retrieval ◽  
Hybrid Model ◽  
Similarity Measure ◽  
Fundamental Problem ◽  
Experimental Results ◽  
Text Similarity ◽  
Mismatch Problem

Word mismatch between queries and documents is a fundamental problem in information retrieval domain. In this article, the authors present an effective approach to Bengali information retrieval that combines two IR models to tackle the word mismatch problem in Bengali IR. The proposed hybrid model combines the traditional word-based IR model with another IR model that uses semantic text similarity measure based on vector embeddings of words. Experimental results show that the performance of our proposed hybrid Bengali IR model significantly improves over the baseline IR model.

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