About this course: This course covers a wide range of tasks in Natural Language Processing from basic to advanced: sentiment analysis, summarization, dialogue state tracking, to name a few. Upon completing, you will be able to recognize NLP tasks in your day-to-day work, propose approaches, and judge what techniques are likely to work well. The final project is devoted to one of the most hot topics in today's NLP. You will build your own conversational chat-bot that will assist with search on StackOverflow website. The project will be based on practical assignments of the course, that will give you hands-on experience with such tasks as text classification, named entities recognition, and duplicates detection.

The belief that humans will be able to interact with computers in conversational speech has long been a favorite subject in science fiction, reflecting the persistent belief that spoken dialogue would be the most natural and powerful user interface to computers. With recent improvements in computer technology and in speech and language processing, such systems are starting to appear feasible. There are significant technical problems that still need to be solved before speech-driven interfaces become truly conversational. This article describes the results of a 10-year effort building robust spoken dialogue systems at the University of Rochester. For example, consider building a telephony system that answers queries about your mortgage.

This special issue of AI Magazine on dialogue with robots brings together a collection of articles on situated dialogue. The contributing authors have been working in interrelated fields of human-robot interaction, dialogue systems, virtual agents, and other related areas and address core concepts in spoken dialogue with embodied robots or agents. Several of the contributors participated in the AAAI Fall Symposium on Dialog with Robots, held in November 2010, and several articles in this issue are extensions of work presented there. The articles in this collection address diverse aspects of dialogue with robots, but are unified in addressing opportunities with spoken language interaction, physical embodiment, and enriched representations of context. Research on computational models and mechanisms for supporting spoken dialogue dates back to the earliest days of AI research, including Alan Turing's reflection about how machine intelligence could be evaluated.

The Dialogue on Dialogues workshop was organized as a satellite event at the Interspeech 2006 conference in Pittsburgh, Pennsylvania, and it was held on September 17, 2006, immediately before the main conference. It was planned and coordinated by Michael McTear (University of Ulster, UK), Kristiina Jokinen (University of Helsinki, Finland), and James A. Larson (Portland State University, USA). The one-day workshop involved more than 40 participants from Europe, the United States, Australia, and Japan. One of the motivations for furthering the systems' interaction capabilities is to improve the AI Magazine Volume 28 Number 2 (2007) ( AAAI) However, relatively little work has so far been devoted to defining the criteria according to which we could evaluate such systems in terms of increased naturalness and usability. It is often felt that statistical speech-based research is not fully appreciated in the dialogue community, while dialogue modeling in the speech community seems too simple in terms of the advanced architectures and functionalities under investigation in the dialogue community.

Many common mistakes can be avoided when testing sentiment data for predictive properties. The term "prediction" is not a legal definition. In assessing the predictive qualities of sentiment data there are no rules for what counts as a signal to be tested for predictive properties with regard to financial assets. However, the method you chose ultimately defines what you mean with the term "prediction". To illustrate the point: Using a more prudent definition of the term, the accuracy in the world's most famous prediction study could have been as low as 47% (7 out of 15) instead of 87% (13 out of 15%).

In essence, sentiment analysis is the process of gauging the emotional tone behind a series of words, used to gain an understanding of the emotions, attitudes and opinions expressed within a customer's online mentions. Real-world examples include the Obama administration using SA to measure public responses to campaign messages ahead of 2012 presidential election, and Expedia Canada taking advantage of SA to quickly understand negative consumer attitudes to the music used in one of their adverts.

Check out "Text Mining with R: A tidy approach" to learn about how tidy data principles and the tidytext package can help you perform text mining in R. "Many of us who work in analytical fields are not trained in even simple interpretation of natural language," write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach. The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document's most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they're using text mining on job postings at Stack Overflow, some of the challenges and best practices they've experienced when mining text, and how their tidytext package for R aims to make text analysis both easy and informative. Text and other unstructured data is increasingly important for data analysts and data scientists in diverse fields from health care to tech to nonprofits. This data can help us make good decisions, but to capitalize on it, we must have the tools and the skills to get from unstructured text to insights.

We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets. There are some general library requirements for the project and some which are specific to individual methods.

Natural language processing (NLP) is a technology spawned from the need for machines to understand and communicate with humans in human language, not formal computer languages. The concept behind NLP is simple: if and when machines can understand and communicate with humans in natural (human) language, it democratizes data science, enabling humans to access, analyze, and leverage data more intelligently and become more efficient as they offload redundant, data-heavy tasks to machines. NLP is most commonly understood as a user interface (UI) technology, enabling two-way communications with computers via speech or text. However, NLP is also a critical technology for extracting insights and analysis from a vast amount of previously unindexed and unstructured data; mining video and audio files, emails, scanned documents, and more. NLP adoption is accelerating, but not because of the creation of new NLP algorithms, as the data science in that regard is mature.

Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram features when learning from textual data. They are also compatible with the use of word embeddings so that word similarities can be accounted for. While the original any-gram kernels are implemented on top of tree kernels, we propose a new approach which is independent of tree kernels and is more efficient. We also propose a more effective way to make use of word embeddings than the original any-gram formulation. When applied to the task of sentiment classification, our new formulation achieves significantly better performance.