![]() ![]() We devise a ranking-based evaluation methodology following both known-item and ad-hoc search scenarios. Along with these models, we also present a specially tailored negative sampling approach that exploits how keywords appear in academic publications. Our techniques adapt the architecture of Word2Vec and FastText to generate keyword embeddings by leveraging documents' keyword co-occurrence. This paper proposes two novel models for the keyword suggestion task trained on scientific literature. In these scenarios, keyword suggestion methods are critical to guide the user during the query formulation. The construction of a detailed query is crucial in some tasks, such as legal retrieval or academic search. However, they often struggle to suggest semantically related keywords given a user's query. Current systems are very good at recommending lexical adaptations or spelling corrections to users' queries. ![]() Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs. Moreover, our work benchmarks several representative Chinese spelling correction models, establishing baselines for future work. Extensive empirical studies have shown significant performance gaps between the open-domain and medical-domain spelling correction, highlighting the need to develop high-quality datasets that allow for Chinese spelling correction in specific domains. This enables one to create the medical misspelling dataset automatically. To ensure automated dataset curation, MCSCSet further offers a medical confusion set consisting of the commonly misspelled characters of given Chinese medical terms. In contrast to the existing open-domain CSC datasets, MCSCSet involves: i) extensive real-world medical queries collected from Tencent Yidian, ii) corresponding misspelled sentences manually annotated by medical specialists. In this work, we define the task of Medical-domain Chinese Spelling Correction and propose MCSCSet, a large scale specialist-annotated dataset that contains about 200k samples. Correcting the misspellings of medical entities is arguably more difficult than those in the open domain due to its requirements of specificdomain knowledge. Despite its extensive use in many applications, like search engines and optical character recognition systems, little has been explored in medical scenarios in which complex and uncommon medical entities are easily misspelled. For More Informationįor more information on daterange or inmeta queries, see "Search Protocol Reference," which is linked to the Google Search Appliance help center.Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. The spell checker cannot be manually edited. For information about installing a language bundle, see the help page for Search > Search Features > Language Bundles. Google occasionally provides new language bundles that offer support for spelling for different sets of languages. Support for these languages is provided by the built-in search appliance language bundle. The spell checker supports US English, Brazilian Portuguese, French, Italian, German, and Spanish. To view spelling suggestions, use the requiredfields parameter instead of inmeta. When using daterange or inmeta queries, spelling suggestions are not returned. Your spell checker may perform differently. Note: The example is specific to the spellĬhecker. However, scuba divers would not return an alternate query Is for gail divers, the phrase gail devers is suggested as an alternative Spelling suggestionsĪre automatically enabled by default. The spell checker detects a possible spelling suggestion. The spell server updates are automatic, so no configuration is required.Ī single spelling suggestion is returned with the results for queries when Periodically, the spell server explores your index to update its database. The spell checker uses data from the documents crawled by the Google Search Appliance to make spelling suggestions. ![]() This page describes the Google Search Appliance feature that checks the spelling of search queries and offers spelling suggestions. Google Search Appliance Admin Console Helpīack to Home | Admin Console Help | Log Out ![]()
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