Cross-language information retrieval

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Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. [1] The term "cross-language information retrieval" has many synonyms, of which the following are perhaps the most frequent: cross-lingual information retrieval, translingual information retrieval, multilingual information retrieval. The term "multilingual information retrieval" refers more generally both to technology for retrieval of multilingual collections and to technology which has been moved to handle material in one language to another. The term Multilingual Information Retrieval (MLIR) involves the study of systems that accept queries for information in various languages and return objects (text, and other media) of various languages, translated into the user's language. Cross-language information retrieval refers more specifically to the use case where users formulate their information need in one language and the system retrieves relevant documents in another. To do so, most CLIR systems use various translation techniques. CLIR techniques can be classified into different categories based on different translation resources: [2]

Contents

CLIR systems have improved so much that the most accurate multi-lingual and cross-lingual adhoc information retrieval systems today are nearly as effective as monolingual systems. [3] "Multilingual Information Access." Understanding Information Retrieval Systems(2011): 373-80. Web.</ref> Other related information access tasks, such as media monitoring, information filtering and routing, sentiment analysis, and information extraction require more sophisticated models and typically more processing and analysis of the information items of interest. Much of that processing needs to be aware of the specifics of the target languages it is deployed in.

Mostly, the various mechanisms of variation in human language pose coverage challenges for information retrieval systems: texts in a collection may treat a topic of interest but use terms or expressions which do not match the expression of information need given by the user. This can be true even in a mono-lingual case, but this is especially true in cross-lingual information retrieval, where users may know the target language only to some extent. The benefits of CLIR technology for users with poor to moderate competence in the target language has been found to be greater than for those who are fluent. [4] Specific technologies in place for CLIR services include morphological analysis to handle inflection, decompounding or compound splitting to handle compound terms, and translations mechanisms to translate a query from one language to another.

The first workshop on CLIR was held in Zürich during the SIGIR-96 conference. [5] Workshops have been held yearly since 2000 at the meetings of the Cross Language Evaluation Forum (CLEF). Researchers also convene at the annual Text Retrieval Conference (TREC) to discuss their findings regarding different systems and methods of information retrieval, and the conference has served as a point of reference for the CLIR subfield. [6] Early CLIR experiments were conducted at TREC-6, held at the National Institute of Standards and Technology (NIST) on November 19–21, 1997. [7]

Google Search had a cross-language search feature that was removed in 2013. [8]

See also

Related Research Articles

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Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.

An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.

<span class="mw-page-title-main">Parallel text</span> Text placed alongside its translation or translations

A parallel text is a text placed alongside its translation or translations. Parallel text alignment is the identification of the corresponding sentences in both halves of the parallel text. The Loeb Classical Library and the Clay Sanskrit Library are two examples of dual-language series of texts. Reference Bibles may contain the original languages and a translation, or several translations by themselves, for ease of comparison and study; Origen's Hexapla placed six versions of the Old Testament side by side. A famous example is the Rosetta Stone, whose discovery allowed the Ancient Egyptian language to begin being deciphered.

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<span class="mw-page-title-main">Text Retrieval Conference</span> Meetings for information retrieval research

The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity, and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology.

Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or "pseudo" feedback.

Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input and expanding the search query to match additional documents. Query expansion involves techniques such as:

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<span class="mw-page-title-main">LGTE</span>

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<span class="mw-page-title-main">Conference and Labs of the Evaluation Forum</span>

The Conference and Labs of the Evaluation Forum, or CLEF, is an organization promoting research in multilingual information access. Its specific functions are to maintain an underlying framework for testing information retrieval systems and to create repositories of data for researchers to use in developing comparable standards. The organization holds a conference every September in Europe since a first constituting workshop in 2000. From 1997 to 1999, TREC, the similar evaluation conference organised annually in the US, included a track for the evaluation of Cross-Language IR for European languages. This track was coordinated jointly by NIST and by a group of European volunteers that grew over the years. At the end of 1999, a decision by some of the participants was made to transfer the activity to Europe and set it up independently. The aim was to expand coverage to a larger number of languages and to focus on a wider range of issues, including monolingual system evaluation for languages other than English. Over the years, CLEF has been supported by a number of various EU funded projects and initiatives.

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<span class="mw-page-title-main">Text, Speech and Dialogue</span>

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<span class="mw-page-title-main">Entity linking</span> Concept in Natural Language Processing

In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is.

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<span class="mw-page-title-main">Sketch Engine</span> Corpus manager and text analysis software

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References

  1. ↑ Wang, Jianqiang, and Douglas W. Oard. "Matching meaning for cross-language information retrieval." Information Processing & Management48.4 (2012): 631-53.
  2. ↑ Tsai, Peishan. "An Introduction to Cross-Language Information Retrieval Approaches". www.mikeandpeishan.com. Archived from the original on 2022-11-04. Retrieved 2022-11-04.
  3. ↑ Oard, Douglas.<ref>teknobot https://www.teknobot.com.tr/2022/12/25/diller-arasi-bilgi-erisimi-cross-language-information-retrieval/.{{cite web}}: Missing or empty |title= (help)
  4. ↑ Airio, Eija (2008). "Who benefits from CLIR in web retrieval?". Journal of Documentation. 64 (5): 760–778. doi:10.1108/00220410810899754.
  5. ↑ The proceedings of this workshop can be found in the book Cross-Language Information Retrieval (Grefenstette, ed; Kluwer, 1998) ISBN   0-7923-8122-X.
  6. ↑ Olvera-Lobo, María-Dolores. "Cross-Language Information Retrieval on the Web." Handbook of Research on Social Dimensions of Semantic Technologies and Web Services(n.d.): 704-19. Web.
  7. ↑ Vorhees, Ellen M.; Harman, Donna (1999). "Overview of the Sixth Text REtrieval Conference (TREC-6)". Information Processing and Management .
  8. ↑ "Google Drops "Translated Foreign Pages" Search Option Due To Lack Of Use". 20 May 2013.


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