In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

/pdf/the-anatomy-of-a-large-scale-hypertextual-web-search-engine-496poj789d.pdf

The anatomy of a large-scale hypertextual Web search engine

Imagined communities: Reflections on the origin and spread of nationalism

The Anatomy of a Large-Scale Hypertextual Web Search Engine.

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

: Training a named entity recognizer (NER) has always been a difficult task due to the effort required to generate a significant amount of annotated training data. In this paper, we reduce or eliminate the effort required to create training data by automatically converting other sources of data into annotated training data. The performance of this approach is tested on a gene-protein name extractor by using the mouse and fly data obtained from the BioCreAtIvE challenge. Results show that our methods are effective and that our trained NER system outperforms all of our baseline results.

/pdf/learning-to-extract-gene-protein-names-from-weakly-labeled-46gmjalel9.pdf

Learning to Extract Gene-Protein Names from Weakly-Labeled Text

Pre-trained word embeddings are the primary method for transfer learning in several Natural Language Processing (NLP) tasks. Recent works have focused on using unsupervised techniques such as language modeling to obtain these embeddings. In contrast, this work focuses on extracting representations from multiple pre-trained supervised models, which enriches word embeddings with task and domain specific knowledge. Experiments performed in cross-task, cross-domain and cross-lingual settings indicate that such supervised embeddings are helpful, especially in the low-resource setting, but the extent of gains is dependent on the nature of the task and domain. We make our code publicly available.

/pdf/supervised-contextual-embeddings-for-transfer-learning-in-4tz72pwnxr.pdf

Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks

Personalization of user experience has a long history of success in the HCI community. More recently the community has focused on adaptive user interfaces, supported by machine learning, that reduce interaction efforts and improves user experience by collapsing transactions and pre-filtering results. However, generally, these more recent results have only been demonstrated in the laboratory environment. In this paper, we share the case of a deployed mobile transit app that adapts based on users’ previous usage. We examine the impact of adaptation, both good and bad, and user abandonment rates. We conducted an 18-month assessment where 2,616 participants (with and without vision impairments) were recruited and participated in an A/B study. Finally, we draw some insights on some unusual effects that appear over the long term.

https://dl.acm.org/doi/pdf/10.1145/3379503.3403536

A Long-Term Evaluation of Adaptive Interface Design for Mobile Transit Information

The mere existence of digital documents on the web makes those documents more accessible to blind people than if they were not digitized. The web provides several opportunities, however, for web authors to describe what content looks like, rather than what it means, and this visual description inconveniences and confuses people using screen readers. We focus on the particular instance of this problem where information that is logically tabular is presented using repeated visual templates. We recruited a group of experienced screen reader users and observed them performing a series of tasks designed to elicit tabular behavior. We find that people who make use of tabular operations perform the tasks faster and more successfully than those who do not. We furthermore find that while many users discover the tables we introduced as part of the study, many others do not or do not choose to make use of the tables. We discuss some techniques for enhancing the discoverability of the introduced tables.

The utility of tables for screen reader users

There is great promise in creating effective technology experiences during situationally-induced impairments and disabilities through the combination of universal design and adaptive interfaces. We believe this combination is a powerful approach for meeting the UX needs of people with disabilities, including those which are temporary in nature. Research in each of these areas, and the combination, illustrates this promise.

Anthony Tomasic

Papers

Learning to Extract Gene-Protein Names from Weakly-Labeled Text

Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing Tasks

A Long-Term Evaluation of Adaptive Interface Design for Mobile Transit Information

The utility of tables for screen reader users

Universal Design and Adaptive Interfaces as a Strategy for Induced Disabilities.