Supervised topic models are often sought to balance prediction quality and in-terpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, interpretable topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models are able to learn much more coherent topics while maintaining competitive predictions.

Dialog State Tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST usually fall into two categories, i.e, the picklist-based and span-based. From one hand, the picklist-based methods perform classifications for each slot over a candidate-value list, under the condition that a pre-defined ontology is accessible. However, it is impractical in industry since it is hard to get full access to the ontology. On the other hand, the span-based methods track values for each slot through finding text spans in the dialog context. However, due to the diversity of value descriptions, it is hard to find a particular string in the dialog context. To mitigate these issues, this paper proposes a Dual Strategy for DST (DS-DST) to borrow advantages from both the picklist-based and span-based methods, by classifying over a picklist or finding values from a slot span. Empirical results show that DS-DST achieves the state-of-the-art scores in terms of joint accuracy, i.e., 51.2% on the MultiWOZ 2.1 dataset, and 53.3% when the full ontology is accessible.

I was given huge autonomy over my project -- which revolved around NLP and sentiment analysis -- and was treated like any other full time employee. Ushur was a perfect place to learn, grow, and jumpstart my career in Silicon Valley.

Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. The recent success of large pre-trained language models such as BERT and GPT -2 (Devlin et al., 2019; Radford et al., 2019) have suggested the effectiveness of incorporating language priors in downstream NLP tasks. However, how much pre-trained language models can help dialog response generation is still under exploration. In this paper, we propose a simple, general, and effective framework: Alternating Recurrent Dialog Model (ARDM). ARDM models each speaker separately and takes advantage of the large pre-trained language model. It requires no supervision from human annotations such as belief states or dialog acts to achieve effective conversations. ARDM outperforms or is on par with state-of-the-art methods on two popular task-oriented dialog datasets: CamRest676 and MultiWOZ. Moreover, we can generalize ARDM to more challenging, non-collaborative tasks such as persuasion. In persuasion tasks, ARDM is capable of generating humanlike responses to persuade people to donate to a charity. It has been a longstanding ambition for artificial intelligence researchers to create an intelligent conversational agent that can generate humanlike responses. Recently data-driven dialog models are more and more popular. However, most current state-of-the-art approaches still rely heavily on extensive annotations such as belief states and dialog acts (Lei et al., 2018). However, dialog content can vary considerably in different dialog tasks. Having a different intent or dialog act annotation scheme for each task is costly. For some tasks, it is even impossible, such as open-domain social chat. Thus, it is difficult to utilize these methods on challenging dialog tasks, such as persuasion and negotiation, where dialog states and acts are difficult to annotate.

Sometimes it happens that brands need to have a sentiment analysis. Knowing how people talk about your brand is essential. What do your community members think about your company? Do they praise you or do they mock of you? Are they sincerely impressed or that enthusiasm hides a sarcastic and brutal critic?

Abstract: With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Though there is an availability of scientometric measures which promise to offer a solution to this problem, these measures are mostly quantitative and rely, for instance, only on the number of times an article is cited. With this approach, it becomes irrelevant if an article is cited 10 times in a positive, negative or neutral way. In this context, it is quite important to study the qualitative aspect of a citation to understand its significance. This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e.g., reference to a dataset, reading reference. Furthermore, the paper also presents two datasets (SentiCiteDB and IntentCiteDB) containing about 2,600 citations with their ground truth for sentiment and nature of citation. SentiCite along with other state-of-the-art methods for sentiment analysis are evaluated on the presented datasets. Evaluation results reveal that SentiCite outperforms state-of-the-art methods for sentiment analysis in scientific publications by achieving a F1-measure of 0.71. 1 INTRODUCTION Sentiment analysis is the process of computationally categorizing and identifying opinions present in a textual document or images. As a field, sentiment analysis has been gaining a lot of interest from the scientific community in recent years. The main motivation for this work comes from the author's observation that there is an unavailability of a system capable of automatically analyzing the sentiment present in citations of scientific publications.

Amazon's newest set of electronic devices was a trending topic in recent times. This use case leverages Data Mining, Natural Language Processing, Machine Learning, and Data Visualization, to build algorithms that perform sentiment analysis on online product reviews and help us understand the consumer sentiments on electronic products available on Amazon. The model can be helpful for any eCommerce business to ascertain the consumer sentiment towards its products and brands. Often, online reviews are large in numbers and are unstructured. Understanding the true intent of the consumers from their reviews can be a difficult task as there arise many barriers such as language ambiguity, sarcasm, irony, and the emojis (emotion icons).

Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in natural language processing in the process.

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Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK. You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.