On the Study of Transformers for Query Suggestion

When conducting a search task, users may find it difficult to articulate their need, even more so when the task is complex. To help them complete their search, search engine usually provide query suggestions. A good query suggestion system requires to model user behavior during the search session. In this article, we study multiple Transformer architectures applied to the query suggestion task and compare them with recurrent neural network (RNN)-based models. We experiment Transformer models with different tokenizers, with different Encoders (large pretrained models or fully trained ones), and with two kinds of architectures (flat or hierarchic). We study the performance and the behaviors of these various models, and observe that Transformer-based models outperform RNN-based ones. We show that while the hierarchical architectures exhibit very good performances for query suggestion, the flat models are more suitable for complex and long search tasks. Finally, we investigate the flat models behavior and demonstrate that they indeed learn to recover the hierarchy of a search session.

HUBUNGAN TINGKAT KEPERCAYAAN DIRI REMAJA AKHIR DENGAN JERAWAT

Jerawat adalah penyakit kulit yang tidak mematikan dan umum terjadi yang bisa dialami oleh 80% masyarakat yang berusia 12 – 14 tahun. Kemunculan jerawat umumnya akan terjadi di usia pubertas (8-9 tahun) dimana pada usia ini produksi hormon androgen meningkat drastis dan mempengaruhi sekresi keratin dan sebum(florentinus, 2014).Tujuan dari penelitian ini adalah untuk mengetahui tingkat kepercayaan diri remaja akhir berhubungan dengan adanya jerawat berdasarkan literature riview. Metode penelitian ini merupakan literature review dengan menggunakan search engine Google Scholar dengan jumlah populasi 1.640 artikel dan sampel sebanyak 6 artikel yang memenuhi kriteria inklusi. Hasil dari penelitian literature review didapatkan ada hubungan tingkat kepercayaan diri dengan tumbuhnya jerawat pada remaja. Kesimpulannya tingkat kepercayaan diri remaja dapat terpengaruhi dengan adanya jerawat, semakin tinggi tingkat keparahan maka semakin kurang dalam kepercayaan diri.

PENGARUH TERAPI MADU TERHADAP PENYEMBUHAN LUKA KAKI DIABETIK

Prevalensi penderita luka kaki diabetik di indonesia terus meningkat setiap tahunnya. Pasien dengan luka kaki diabetik memerlukan perawatan jangka panjang dan pemilihan terapi yang tepat untuk dapat sembuh kembali. Salah satu terapi yang sering di lakukan dalam perawatan luka adalah dengan terapi madu. Tujuan penelitian ini adalah untuk mengetahui pengaruh pemberian madu terhadap penyembuhan luka kaki diabetik. Metode yang digunakan dalam penelitian ini adalah Literature Review dengan cara melakukan pencarian artikel dengan mengakses jurnal dari internet dengan Search engine Google Scholar dan FreeFullPDF terdiri dari 2.345 populasi dan di dapat 10 jurnal fulltext yang sesuai dengan kriteria inklusi dan ekslusi. Hasil literature review menunjukan bahwa madu sangat efektik dalam penyembuhan luka kaki diabetik. Hasil menunjukan pemberian madu dengan beberapa cara yaitu ditetes, dioles, dikompres dan dikombinasikan dengan habbatus sauda dan minyak zaitun menunjukan adanya peningkatan derajat luka, epitelisasi dan granulasi berdasarkan metode DESIGN dan skala BJWAT. Madu memiliki sifat lembab/moist yang sangat baik untuk penyembuhan luka. Literature review ini dapat dijadikan dasar bagi peneliti selanjutnya untuk melakukan penelitian primer pemberian madu secara langsung terhadap perawatan luka kaki diabetik.

A New Content Based Search Mechanism for Image Retrieval Search Engine

In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% accuracy to retrieve the intended image by the user. But it is found that due to the efficiency of the applied algorithm, in all cases, the retrieved images are with the same accuracy irrespective of the input query image accuracy. This implementation is very much useful in the fields of forensic, defense and diagnostics system in medical field etc. .

Why-type Question to Query Reformulation for efficient Document Retrieval

Understanding the actual need of user from a question is very crucial in non-factoid why-question answering as Why-questions are complex and involve ambiguity and redundancy in their understanding. The precise requirement is to determine the focus of question and reformulate them accordingly to retrieve expected answers to a question. The paper analyzes different types of why-questions and proposes an algorithm for each class to determine the focus and reformulate it into a query by appending focal terms and cue phrase ‘because’ with it. Further, a user interface is implemented which asks input why-question, applies different components of question , reformulates it and finally retrieve web pages by posing query to Google search engine. To measure the accuracy of the process, user feedback is taken which asks them to assign scoring from 1 to 10, on how relevant are the retrieved web pages according to their understanding. The results depict that maximum precision of 89% is achieved in Informational type why-questions and minimum of 48% in opinionated type why-questions.

Natural Language Processing–Assisted Literature Retrieval and Analysis for Combination Therapy in Cancer

PURPOSE Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain.

Query Sense Discovery Approach to Realize the User's Search Intent

The main goal of information retrieval is getting the most relevant documents to a user’s query. So, a search engine must not only understand the meaning of each keyword in the query but also their relative senses in the context of the query. Discovering the query meaning is a comprehensive and evolutionary process; the precise meaning of the query is established as developing the association between concepts. The meaning determination process is modeled by a dynamic system operating in the semantic space of WordNet. To capture the meaning of a user query, the original query is reformulating into candidate queries by combining the concepts and their synonyms. A semantic score characterizing the overall meaning of such queries is calculated, the one with the highest score was used to perform the search. The results confirm that the proposed "Query Sense Discovery" approach provides a significant improvement in several performance measures.

Intelligent Resume Retrieval Based on Lucence

With the development of Internet, the electronic resume has gradually replaced the paper one. It is the basic requirement of recruitment for enterprises to retrieve the talent information that fulfills the requirement quickly and without omission.Based on the framework of SpringBoot and Lucence full-text search engine, this paper implements a resume intelligent filtering algorithm, which improves the query speed of the system by establishing an index database. At the same time,the scoring function improves the accuracy of the filtering results, reduces the pressure of high concurrency of the database, improves the work efficiency of the Human Resources Department, and avoids the talent loss.

Artificial Intelligence in Retail

This chapter gives an overview of how artificial intelligence is used by the retail sector to enhance customer experience and to improve profitability. It provides information about the role of the pandemic in stimulating AI adoption by retailers. It deliberates on how AI tools help retailers to engage customers online and in stores. Firms gain better understanding of customers, design immersive experiences, and enhance customer lifetime value using cost-effective technology solutions. It discusses popular AI algorithms like recommendation algorithm, association algorithm, classification algorithm, and predictive algorithm. Popular applications in retail include chatbots, visual search, voice search engine optimisation, in-store assistance, and virtual fitting rooms.