Incorporating terminology evolution for query translation in text retrieval with association rules

Kaluarachchi, Amal; Varde, Aparna S.; Bedathur, Srikanta; Weikum, Gerhard; Peng, Jing; Feldman, Anna

doi:10.1145/1871437.1871730

Cited by 22 publications

(21 citation statements)

References 7 publications

(6 reference statements)

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“…The approach requires a recurrent computation which can affect efficiency and scalability. Kaluarachchi et al [37,38] proposed to discover semantically identical concepts (or named entities) that are used at different times using an association rule mining technique using events (sentences containing a subject, a verb, objects, and nouns) associated to two distinct entities. Two entities are semantically related if the associated events occur multiple times in a document archive.…”

Section: Application Scenariosmentioning

confidence: 99%

Mining semantics for culturomics

Borin

Dubhashi

Forsberg

et al. 2013

Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing

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The massive amounts of text data made available through the Google Books digitization project have inspired a new field of big-data textual research. Named culturomics, this field has attracted the attention of a growing number of scholars over recent years. However, initial studies based on these data have been criticized for not referring to relevant work in linguistics and language technology. This paper provides some ideas, thoughts and first steps towards a new culturomics initiative, based this time on Swedish data, which pursues a more knowledge-based approach than previous work in this emerging field. The amount of new Swedish text produced daily and older texts being digitized in cultural heritage projects grows at an accelerating rate. These volumes of text being available in digital form have grown far beyond the capacity of human readers, leaving automated semantic processing of the texts as the only realistic option for accessing and using the information contained in them. The aim of our recently initiated research program is to advance the state of the art in language technology resources and methods for semantic processing of Big Swedish text and focus on the theoretical and methodological advancement of the state of the art in extracting and correlating information from large volumes of Swedish text using a combination of knowledge-based and statistical methods.

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Section: Application Scenariosmentioning

confidence: 99%

Mining semantics for culturomics

Borin

Dubhashi

Forsberg

et al. 2013

Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing

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“…The affect of terminology evolution over time is addressed in [13,28,55,56,118]. In [13], Berberich et al proposed a method based on a hidden Markov model for reformulating a query into terminology prevalent in the past.…”

Section: Searching With the Awareness Of Terminology Changesmentioning

confidence: 99%

“…In [13], Berberich et al proposed a method based on a hidden Markov model for reformulating a query into terminology prevalent in the past. Kaluarachchi et al [55,56] studied the problem of concepts (or entities) whose names can change over time. They proposed to discover concepts that evolve over time using association rule mining, and used the discovered concepts to translate time-sensitive queries and answered appropriately.…”

Section: Searching With the Awareness Of Terminology Changesmentioning

confidence: 99%

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Time-aware approaches to information retrieval

Kanhabua

2012

SIGIR Forum

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In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

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“…The focus of SITAC to discover rules of the type (C1,T1)=> (C2,T2), i.e., concept C1 at time T1 implies concept C2 at time T2, to serve as the basis for time-aware query translation. This system is based on a novel solution approach that involves an integration of natural language processing, association rule mining and contextual similarity [5,6]. It intelligently simulates human thinking for query processing, because humans intuitively tend to associate concepts that are semantically identical when answering questions about such temporally altering terms [6].…”

Section: Introductionmentioning

confidence: 99%

Sitac

Kaluarachchi

Roychoudhury

Varde

et al. 2011

Proceedings of the 14th International Conference on Extending Database Technology

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This paper demonstrates a system called SITAC based on our proposed approach to automate the discovery of concepts (called SITACs) in text sources that are identical semantically but alter their names over time. This system is developed to perform timeaware translation of queries over text corpora by incorporating terminology evolution, thus providing more accurate responses to users, e.g., query processing on Mumbai should automatically take into account its former name Bombay. The SITAC system constitutes a novel collaborative framework of natural language processing, association rule mining and contextual similarity.

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Incorporating terminology evolution for query translation in text retrieval with association rules

Cited by 22 publications

References 7 publications

Mining semantics for culturomics

Mining semantics for culturomics

Time-aware approaches to information retrieval

Sitac

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