TR-SEQ: Named Entity Recognition Dataset for Turkish Search Engine Queries

Recognizing named entities in short search engine queries is a difficult task due to their weaker contextual information compared to long sentences. Standard named entity recognition (NER) systems that are trained on grammatically correct and long sentences fail to perform well on such queries. In this study, we share our efforts towards creating a cleaned and labeled dataset of real Turkish search engine queries (TR-SEQ) and introduce an extended label set to satisfy the search engine needs. A NER system is trained by applying the state-of-the-art deep learning method BERT to the collected data and its high performance on search engine queries is reported. Moreover, we compare our results with the state-of-the-art Turkish NER systems.

Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships

Word embedding techniques depend heavily on the frequencies of words in the corpus, and are negatively impacted by failures in providing reliable representations for low-frequency words or unseen words during training. To address this problem, we propose an algorithm to learn embeddings for rare words based on an Internet search engine and the spatial location relationships. Our algorithm proceeds in two steps. We firstly retrieve webpages corresponding to the rare word through the search engine and parse the returned results to extract a set of most related words. We average the vectors of the related words as the initial vector of the rare word. Then, the location of the rare word in the vector space is iteratively fine-tuned according to the order of its relevances to the related words. Compared to other approaches, our algorithm can learn more accurate representations for a wider range of vocabulary. We evaluate our learned rare-word embeddings on the word relatedness task, and the experimental results show that our algorithm achieves state-of-the-art performance.

Building a kernel of Search Engine overrule in search and indexing

This research designs web search engine kernel overrule in searching of specific fields and indexing indicated sites. This research contain information about search in web , retrieval system , types of search engines and basic architectures of building search engines .It suggests search engine architecture kernel of dedicated search engine to do final planner of search engine architecture ,and build parts of search engine and execute test to get results .

Using Open Source in Enhancing Information Retrieval Systems Performance

There are many methods and suggestions proposed to improve the efficiency of search in order to catch up with the increasing speed of information boom on the web. Most of these proposals are concentrated on term frequency and page rank algorithms and yet very few of them focus on semantic relationship of content. The objective of this research project is to provide a semantic relationship model that can be used for semi-structured or unstructured information on the web to help improve the accuracy and efficiency of search engine.