Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

计量经济分析 = Econometric analysis

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.

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Neural Architectures for Named Entity Recognition

Political identity is often manifested in language variation, but the relationship between the two is still relatively unexplored from a quantitative perspective. This study examines the use of Catalan, a language local to the semi-autonomous region of Catalonia in Spain, on Twitter in discourse related to the 2017 independence referendum. We corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independence tweets. We also find that Catalan is used more often in referendum-related discourse than in other contexts, contrary to prior findings on language variation. This suggests a strong role for the Catalan language in the expression of Catalonian political identity.

Sí o no, què penses? Catalonian Independence and Linguistic Identity on Social Media

Whitespace errors are common to digitized archives. This paper describes a lightweight unsupervised technique for recovering the original whitespace. Our approach is based on count statistics from Google n-grams, which are converted into a likelihood ratio test computed from interpolated trigram and bigram probabilities. To evaluate this approach, we annotate a small corpus of whitespace errors in a digitized corpus of newspapers from the 19th century United States. Our technique identifies and corrects most whitespace errors while introducing a minimal amount of oversegmentation: it achieves 77% recall at a false positive rate of less than 1%, and 91% recall at a false positive rate of less than 3%.

Correcting Whitespace Errors in Digitized Historical Texts.

Diachronic word embeddings—vector representations of words over time—offer remarkable insights into the evolution of language and provide a tool for quantifying sociocultural change from t...

Follow the leader: Documents on the leading edge of semantic change get more citations

In human-human dialogues, face-to-face meetings are often preferred over phone conversations. One explanation is that non-verbal modalities such as gesture provide additional information, making communication more efficient and accurate. If so, computer processing of natural language could improve by attending to non-verbal modalities as well. We consider the problem of sentence segmentation, using hand-annotated gesture features to improve recognition. We find that gesture features correlate well with sentence boundaries, but that these features improve the overall performance of a language-only system only marginally. This finding is in line with previous research on this topic. We provide a regression analysis, revealing that for sentence boundary detection, the gestural features are largely redundant with the language model and pause features. This suggests that gestural features can still be useful when speech recognition is inaccurate.

/pdf/gestural-cues-for-sentence-segmentation-39893tgyl7.pdf

Gestural Cues for Sentence Segmentation

This paper explores the relationship between discourse segmentation and coverbal gesture. Introducing the idea of gestural cohesion, we show that coherent topic segments are characterized by homogeneous gestural forms and that changes in the distribution of gestural features predict segment boundaries. Gestural features are extracted automatically from video, and are combined with lexical features in a Bayesian generative model. The resulting multimodal system outperforms text-only segmentation on both manual and automaticallyrecognized speech transcripts.

/pdf/gestural-cohesion-for-topic-segmentation-2ea2dteige.pdf

Jacob Eisenstein

Papers

Sí o no, què penses? Catalonian Independence and Linguistic Identity on Social Media

Correcting Whitespace Errors in Digitized Historical Texts.

Follow the leader: Documents on the leading edge of semantic change get more citations

Gestural Cues for Sentence Segmentation

Gestural Cohesion for Topic Segmentation