Monolingual Queries Research Articles

Learning to Translate Kannada and English Queries for Mixed Script Information Retrieval

Due to increase in the availability of numerous languages in the Web, cross language information retrieval is one of the happening issues in the field of natural language processing and information retrieval. Nowadays, people are habituated to combine two or more language words during oral or written discourse. Speakers have also employed intermixing of different languages and scripts in digital media while querying, blogging and on social media platforms. The way of representing two different language words of an utterance in their native scripts is known as mixed scripting. In the present work, we attempted to translate mixed script queries of Kannada and English languages into monolingual queries. We proposed three approaches for translation by constructing bilingual dictionary, word embeddings and Google translate. The proposed method outperforms the conventional dictionary based approach, when word embeddings were combined with the translations learnt from Google Translate and Dictionary.

Analysis of translated query in Quranic Malay and English translation documents with stemmer

Quranic documents result has a limited query due to focusing on exact words to retrieve those relevant documents. Therefore, there is variety of results to be useful for the target users to explore Quran documents in proper manner. Thus, this paper presents analysis according to conducted empirical experiments in 12 retrieval processes. Thus a system is needed to retrieve relevant documents across language boundaries as well as monolingual. Therefore, empirical experiments are conducted with the purposes to investigate English-Malay translation approach and vice versa against monolingual searching process. Furthermore, it is also conducted to investigate the performance between keywords and querywords based on total retrieve and relevant for each retrieval process. The retrieval however, included the unnecessary documents because of the translation polysemy. This research also is being applied in retrieving Quran English and Malay translated documents with queries compared to monolingual query searching retrieval. Furthermore, in order to produce more significant result, the comparison between stemmer and monolingual results are successfully analysed to evaluate precision and recall percentages. The most important findings are the use of stemmer more beneficial to the query and documents simultaneously regardless the experiments applied translation or not. It leads more and more relevant results displayed.

Analyzing chinese-english mixed language queries in a web search engine

With the increasing number of multilingual web pages on the Internet, multilingual information retrieval has become an important research issue. While queries are the key element of information retrieval process, mixed-language queries have not yet been adequately studied. This study investigates the search topics and user intents of Chinese-English mixed language queries submitted to a Chinese web search engine, and develops a typology of English terms used in those mixed language queries. The preliminary findings show that the category of search topics and distribution of user intents differ from those of monolingual queries reported in previous studies, suggesting a specific searching behavior of Chinese-English mixed language queries users. The findings of this study could provide useful insights in understanding specific searching behavior of Chinese-English mixed language queries users, and inform the construction of controlled vocabularies and cross-lingual query expansion.

A Cross-Lingual Pattern Retrieval Framework

We introduce a method for learning to grammatically categorize and organize the contexts of a given query. In our approach, grammatical descriptions, from general word groups to specific lexical phrases, are imposed on the query's contexts aimed at accelerating lexicographers' and language learners' navigation through and GRASP upon the word usages. The method involves lemmatizing, part-of-speech tagging and shallowly parsing a general corpus and constructing its inverted files for monolingual queries, and word-aligning parallel texts and extracting and pruning translation equivalents for cross-lingual ones. At run-time, grammar-like patterns are generated, organized to form a thesaurus index structure on query words' contexts, and presented to users along with their instantiations. Experimental results show that the extracted predominant patterns resemble phrases in grammar books and that the abstract-to-concrete context hierarchy of querying words effectively assists the process of language learning, especially in sentence translation or composition.

Experiments with dictionary-based CLIR using graded relevance assessments: Improving effectiveness by pseudo-relevance feedback

Research on cross-language information retrieval (CLIR) has typically been restricted to settings using binary relevance assessments. In this paper, we present evaluation results for dictionary-based CLIR using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. First, monolingual baseline queries were automatically formed from the topics. Secondly, source language topics (in English, German, and Swedish) were automatically translated into the target language (Finnish), using structured target queries. The effectiveness of the translated queries was compared to that of the monolingual queries. Thirdly, pseudo-relevance feedback was used to expand the original target queries. CLIR performance was evaluated using three relevance thresholds: stringent, regular, and liberal. When regular or liberal threshold was used, a reasonable performance was achieved. Using stringent threshold, equally high performance could not be achieved. On all the relevance thresholds the performance of the translated queries was successfully raised by pseudo-relevance feedback based query expansion. However, the performance of the stringent threshold in relation to the other thresholds could not be raised by this method.

The Effects of Conjunction, Facet Structure, and Dictionary Combinations in Concept-Based Cross-Language Retrieval

The paper studies concept-based cross-language information retrieval (CLIR). The document collection was a subset of the TREC collection. The test requests were formed from TREC‘s health related topics. As translation dictionaries the study used a general dictionary and a domain-specific (emedical) dictionary. The effects of translation method, conjunction, and facet order on the effectiveness of concept-based cross-language queries were studied, and concept-based structuring of cross-language queries was compared to mechanical structuring based on the output of dictionaries. The performance of translated Finnish queries against English documents was compared to the performance of original English queries against the English documents, and the performance of different CLIR query types was compared with one another. No major difference was found between concept-based and mechanical structuring. The best translation method was a simultaneous look-up in the medical dictionary and the general dictionary, in which case cross-language queries performed as well as the original English queries. The results showed that especially at high exhaustivity (the number of mutually restrictive concepts in a request) levels cross-language queries perform well in relation to monolingual queries. This suggests that conjunction disambiguates cross-language queries. An extensive study was made of the relative importance of the concepts of requests. On the basis of the classification data of request concepts it was shown how the order of facets in a query affects cross-language as well as monolingual queries.