If you have a project or service that requires scraping search results for data, you might be interested in this API that can streamline the process. Zenserp is able to get real-time data from search results on the major search platforms. Their simple API has scalable options that make it a great solution for any sized project. You can try Zenserp for free, to see how powerful this API is. Get detailed scrape results from APIs for specific situations.

The Linked Open Data practice has led to a significant growth of structured data on the Web in the last decade. Such structured data describe real-world entities in a machine-readable way, and have created an unprecedented opportunity for research in the field of Natural Language Processing. However, there is a lack of studies on how such data can be used, for what kind of tasks, and to what extent they can be useful for these tasks. This work focuses on the e-commerce domain to explore methods of utilising such structured data to create language resources that may be used for product classification and linking. We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pre-training of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords. Our evaluation on an extensive set of benchmarks shows word embeddings to be the most reliable and consistent method to improve the accuracy on both tasks (with up to 6.9 percentage points in macro-average F1 on some datasets). The other two methods however, are not as useful. Our analysis shows that this could be due to a number of reasons, including the biased domain representation in the structured data and lack of vocabulary coverage. We share our datasets and discuss how our lessons learned could be taken forward to inform future research in this direction.

Is search engine optimization part of social media? How does social media affect search engine ranking? Does search engine optimization really work? How do search engines optimize search results? Jul 24, 2020 -- Although social media is not a ranking factor, it has a link to search engine optimization.

AI-based visual search solution has the potential to change our interactions with the world around us. , Quytech develops an AI-powered visual search solution to enhance the customer experience.

Can machines learn to use a search engine as an interactive tool for finding information? That would have far reaching consequences for making the world's knowledge more accessible. This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based generative language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that can learn interactive search strategies completely from scratch. In both cases, we obtain significant improvements over one-shot search with a strong information retrieval baseline. Finally, we provide an in-depth analysis of the learned search policies.

Retrieval with dense, fully-learned representations has the potential to address some fundamental challenges in sparse retrieval. Dense retrieval systems conduct first-stage retrieval using embedded For example, vocabulary mismatch can be solved if the embeddings representations and simple similarity metrics to match a query accurately capture the information need behind a query and to documents. Its effectiveness depends on encoded embeddings maps it to relevant documents. However, decades of IR research to capture the semantics of queries and documents, a challenging demonstrates that inferring a user's search intent from a concise task due to the shortness and ambiguity of search queries. This and often ambiguous search query is challenging [7]. Even with paper proposes ANCE-PRF, a new query encoder that uses pseudo powerful pre-trained language models, it is unrealistic to expect an relevance feedback (PRF) to improve query representations for encoder to perfectly embed the underlying information need from dense retrieval. ANCE-PRF uses a BERT encoder that consumes a few query terms.

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Keyword extraction also known as keyword detection is a machine learning technique that can help you automate the identification and extraction of relevant information from unstructured text data. BytesView's efficient keyword extraction tool can analyze unstructured text including customer feedback, emails, surveys, social media posts, etc. Pre-define tags to identify topical content, business intelligence, customer opinions, and recurring tickets.

According to a report from Ped30, they have gotten their hands on an investor's note from Bernstein's analysts where they are claiming that Google is now paying Apple as much as $15 billion in 2021 to remain Safari's default search. This is higher than what Google had paid Apple in 2020 at $10 billion, and it seems that this figure is only expected to grow. According to the analysts, "We now estimate that Google's payments to AAPL to be the default search engine on iOS were $10B in FY 20, higher than our prior published model estimate of $8B. Recent disclosures in Apple's public filings as well as a bottom-up analysis of Google's TAC (traffic acquisition costs) payments each point us to this figure…We now forecast that Google's payments to Apple might be nearly $15B in FY 21, contribute an amazing 850 bps to Services growth YoY, and amount to 9% of company gross profits." They go on to estimate that this figure will jump to $18-$20 billion in 2022, and the reason behind the increase in payments is because Google wants to ensure that Microsoft (and other competitors) don't outbid them.

Multiclass multilabel classification refers to the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of that multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy, logistic, etc.). Empirically, these methods have been reported to achieve good performance on different metrics (F1 score, Recall, Precision, etc.). Theoretically though, the multilabel classification reductions does not accommodate for the prediction of varying numbers of labels per example and the underlying losses are distant estimates of the performance metrics. We propose a loss function, sigmoidF1. It is an approximation of the F1 score that (I) is smooth and tractable for stochastic gradient descent, (II) naturally approximates a multilabel metric, (III) estimates label propensities and label counts. More generally, we show that any confusion matrix metric can be formulated with a smooth surrogate. We evaluate the proposed loss function on different text and image datasets, and with a variety of metrics, to account for the complexity of multilabel classification evaluation. In our experiments, we embed the sigmoidF1 loss in a classification head that is attached to state-of-the-art efficient pretrained neural networks MobileNetV2 and DistilBERT. Our experiments show that sigmoidF1 outperforms other loss functions on four datasets and several metrics. These results show the effectiveness of using inference-time metrics as loss function at training time in general and their potential on non-trivial classification problems like multilabel classification.