Holmes’ Approach:
Sherlock Holmes’ reasoning method relies on balancing probabilities and choosing the most likely outcome, making him a Bayesian probabilist rather than a pure logician. Holmes’ success stems from his superior data and attention to it, not necessarily his intellectual superiority.

Data and Probability in Computational Linguistics:
In computational linguistics, the use of statistical and probabilistic methods has seen a significant rise, from none in 1979 to over half by 2009.

More Data, Better Results:
A study by Banco and Brill demonstrates that adding more training data leads to better results in word disambiguation tasks. As the amount of training data increases, even the worst algorithm can outperform the best algorithm with less data.

The Internet as a Vast Data Source:
The internet contains an immense amount of data, estimated to be 10 to the power of 14 words. Google harvested a trillion words of English from the internet, contributing to the Linguistic Data Consortium (LDC).

The Trillion Word Corpus:
The trillion word corpus contains 95 billion sentences and 13 million unigrams, including numbers, misspellings, and proper names. It provides insights into word usage and frequency, enabling applications like spelling correction.

From Simple to Non-Parametric Models:
The field of computational linguistics has evolved from simple parametric models to semi-parametric models like neural nets and non-parametric methods like nearest neighbors. Non-parametric methods have a number of parameters equal to the number of data points, allowing every data point to contribute to the model.

Blank Slate Models:
Modern computational linguistics models are often blank slate models, where both the structure and parameters are learned from the data. This approach reduces the experimenter’s role in model design and allows the data to play a more significant role.

Applications of Data in Computational Linguistics:
The trillion word corpus and vast internet data provide opportunities for various applications in computational linguistics, such as machine translation, speech recognition, and information retrieval.

Google Set:
Google Set is a clustering algorithm that allows users to explore relationships between concepts by inputting examples. The algorithm returns a set of related concepts, with those closest to the centroid (central point) of the set appearing near the front. Users can input diverse concepts, such as artists, animals, or commands, to discover connections and patterns.

Exploring Diverse Data Sources:
Google Set can be used to analyze various types of data, including concrete nouns, comparative adjectives, and even non-English terms. By inputting different combinations of terms, users can uncover hidden relationships and insights within the data. The algorithm’s ability to handle diverse data sources makes it a valuable tool for exploring complex concepts and identifying patterns.

User Data:
In addition to web data, Google has access to user data, which provides valuable insights into user behavior and preferences. Analyzing user data can help businesses understand how users interact with their products and services, identify areas for improvement, and personalize user experiences.

1. Google Trends:
A tool that allows users to explore search trends over time and across different regions. Examples: “full moon” and “watermelon” queries show a 28-day cycle, indicating the moon’s influence on agriculture. “machine learning” queries are consistently high, but more prominent in Asian countries, suggesting growing interest in the field. Comparisons: Google Trends can compare different search terms, such as “MySpace,” “YouTube,” “Wikipedia,” and “Microsoft,” to show their relative popularity over time.

2. Enhancing User Experience:
Providing relevant results even when users make spelling mistakes. Offering question answering features by extracting facts from the web and displaying them at the top of the search results.

3. Extracting Facts:
Approaches for extracting facts: Specific patterns for individual websites: Define regular expressions to match specific data formats. Drawbacks: manual work, site changes can break the patterns. General patterns across multiple websites: Develop patterns that work within sentences to extract data. Possible averaging of results from different sources. Learning patterns from examples: Use machine learning to identify patterns without manual intervention. Learning relations: Understand the relationships between different types of data to extract meaningful information.

Seed Facts and Matching Sentences:
To learn patterns, we start with seed facts and match them with sentences from a corpus. We divide the matching sentence into parts that match the arguments and parts that surround it.

Generalizing Patterns:
We generalize the parts that were in black (surrounding parts) rather than red (matching parts). This helps us learn categories such as months, nationalities, and other auxiliary verbs.

Extracting Birth Facts:
In an experiment with 100 million web pages, we extracted a million birth facts with 90% accuracy. The most productive pattern was a concept labeled C4 (auxiliary verbs) followed by “born” and “in.”

Precision of Learned Patterns:
Sampling the 10,000 patterns the system was most confident about yielded 98.5% precision. Precision decreased as we considered less confident patterns, reaching 75% for the last of the million patterns.

Learning Classes and Attributes:
We explored learning classes and their attributes by examining a diagram. One target class included Earth, Mercury, Venus, Saturn, Sirius, and Antares, with attributes like size, temperature, and composition. Other classes included computer games with attributes like makers and storyline, and companies with attributes like CEO and market share.

Overall Accuracy:
The system demonstrated promising accuracy in learning concepts, relations, and patterns from text, including extracting birth facts and identifying classes and their attributes.

Sources for Attributes Extraction:
Documents on the web provide comprehensive and authoritative information but are complex and challenging to understand due to long English sentences. User queries are concise and focused, providing valuable insights into the attributes of interest. Comparison of attributes extracted from web documents and user queries reveals differences in focus. Documents emphasize manufacturers, dosages, and effectiveness, while queries focus on side effects, half-life, and mechanism of action.

Statistical Machine Translation:
Statistical machine translation utilizes data-driven approaches to translate text, avoiding the need for manually crafted rules and grammars. The process involves collecting parallel text, aligning sentences and words, and estimating translation probabilities. Statistical models are employed to assign probabilities to translation options, considering both the translation model and the language model. The system struggles with disfluencies and fluency issues in the translated sentences.

Translation Model:
The translation model is based on counting co-occurrences of words in parallel text. Grouping words into phrases improves translation accuracy. Statistical models outperform grammatical models in handling exceptions and general cases. Expanding the training data from three words to seven words and a trillion words improves translation quality.

Feature Selection:
Various features, including parse trees, part-of-speech tagging, and categories from different databases, were tested for their contribution to translation accuracy. Only raw counts from the data were found to be beneficial, indicating the effectiveness of data-driven approaches.

Tricks for Efficient Data Storage:
Minimizing Bits for Probability Storage: Experimentally determined that four bits are optimal for storing probabilities, beyond which performance does not improve significantly. Efficient Word Alignment: Employing stemming or truncation techniques to reduce memory usage for word alignment. Truncation Optimization: Found that truncating words to four characters yielded the best trade-off between space-saving and performance.

Data’s Importance and Limitations:
Data Abundance: Having vast amounts of data is crucial for core search services, derivative services, and future advancements. Data’s Impersonal Nature: Despite the impersonal nature of data and the internet, machine learning models should focus on connecting writers and consumers.

Purpose of Machine Learning in Search:
Facilitating Connections: Machine learning should aim to establish connections between searchers and writers, understanding their needs and intentions. Augmenting Intelligence: Machine learning should augment human intelligence rather than replacing it, enhancing the connection between writers and consumers.

Gutenberg Press:
Sebastian Brandt, an early press operator, noted the shift in book ownership from only the wealthy to anyone, leading to increased knowledge among the general population.

Public Libraries:
Ben Franklin’s promotion of public libraries provided access to thousands of books for common people, contributing to their intelligence and awareness.

Internet:
Bill Clinton highlighted the internet’s impact on work, learning, and communication, making information more widely available and accessible.

Data Analysis and Financial Markets:
The question of using theories from financial data analysis in the field of information access was raised. Challenges in beating the market were discussed, as everyone having the same information reduces individual advantage.

Assisting Authors:
The potential for tools to help writers understand user interests and tailor their content was discussed. Google Trends and live trends provide some insights into user interests, but more sophisticated feedback mechanisms could be developed.

Analytic Tools for Writers:
Google provides analytic tools to help writers understand readership, but further analysis of why people read or don’t read certain content could be valuable.

Machine Translation Limitations:
Automated translation is improving, but there are limitations due to real-world factors. Language-specific nuances, such as gender agreement in pronouns, can lead to errors. Understanding physical implications from text alone is challenging, impacting accurate translation.

Combining Human and Automated Input:
Human input can help improve accuracy by post-editing automated translations. Achieving 100% automated translation with perfect accuracy may be difficult.

Google’s Goog411 and Data Collection:
The speculation that Goog411 was introduced to gather spoken data is incorrect. Google has various efforts focused on data beyond text, including spoken data.

Google 411 Service:
Google 411 was created as a valuable service to users and to connect local products. The service gathers training data through user interactions, enabling continuous improvement.

Non-Textual Data Focus:
Google has primarily focused on textual data in the past. The company is now expanding its focus to include non-textual data, such as maps, images, and videos.

Image Analysis:
Google is beginning to analyze images, including still images and video images from Google Video and YouTube. Future developments in this area are expected.

Speech-to-Text Techniques:
Speech-to-text translation involves generating a speech waveform from text and ensuring the waveform’s fluency. The same techniques used in text translation can be applied to speech-to-text with appropriate data gathering.

Spam Detection:
A Google Data API that can receive submissions of comments, forum posts, or blogs and provide spam detection responses would be useful for small sites with limited resources. This API could help prevent spam from being published.

Spam Detection and Filtering:
Google is aware of the incentive for comment spam even after implementing the nofollow tag, as users may still gain eyeballs without acquiring links or page rank. The possibility of creating a service to score the spamminess of submitted content was raised, but its feasibility and effectiveness require further exploration.

Assessing User Satisfaction with Search Results:
Measuring the difference between finding something and being satisfied with the results is a challenging task for search engines. Methods like providing feedback buttons in the toolbar have not been very effective, as users tend to only provide feedback when they are upset. Google relies on observing user clicks to infer their satisfaction, but this method is not foolproof.

Evaluating Translation Quality:
Google encountered a problem where some Arabic pages used by their translation system were not real Arabic pages but rather machine-generated content. They addressed this issue by separating the good pages from the bad pages and avoided training on the bad pages to ensure the quality of their translation models.

Dealing with Machine Translation Abuse:
Some individuals use machine translation services to translate pages from one language to another and publish them as original content, aiming to profit from this practice. Google employs spam detection techniques to identify and exclude such pages from their training data, mitigating the impact of machine translation abuse on their translation models.

Language Translation:
Different data sources use different language styles, such as news sites versus blogs. Google has created submodels to address the consistent language type found in news articles. Google is still working on distinguishing between slang and non-slang language.

OCR and Language Models:
Google uses OCR systems for the book scanning project. OCR systems are prone to errors, which Google corrects using language models. Having a better model of what makes sense in a language improves OCR output.

Conclusion:
Google Developer Day was a successful event with many attendees. Peter Norvig thanks everyone for attending his talk and the event.