IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation. Further, IBM is bringing technology from IBM Research for understanding business documents, such as PDF's and contracts, to also add to their AI models.

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation.

In natural language the intended meaning of a word or phrase is often implicit and depends on the context. In this work, we propose a simple yet effective method for sentiment analysis using contextual embeddings and a self-attention mechanism. The experimental results for three languages, including morphologically rich Polish and German, show that our model is comparable to or even outperforms state-of-the-art models. In all cases the superiority of models leveraging contextual embeddings is demonstrated. Finally, this work is intended as a step towards introducing a universal, multilingual sentiment classifier.

I made it with Max last night! OMG! Welcome to womanhood!! How was it/he? And right about now, Mary's mom gets a'notification' on her cell phone that her daughter is texting sexual references, then displays Mary's texts with Shelly upon mom's request. Mom spends the rest of the day at work fuming, conjuring dialog with her daughter for later that evening when they'll be home together. Never did, and she'd told Mary not to see him.

Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi-domain dialogue system to accomplish complex users' goals under tourist information desk settings. This paper describes our submitted solution, Hierarchical Context Enhanced Dialogue System (HCEDS), for this task. The main motivation of our system is to comprehensively explore the potential of hierarchical context for sufficiently understanding complex dialogues. More specifically, we apply BERT to capture token-level information and employ the attention mechanism to capture sentence-level information. The results listed in the leaderboard show that our system achieves first place in automatic evaluation and the second place in human evaluation.

Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as ``bank.'' In this paper, we show that Gaussian latent Dirichlet allocation could recover the ability to capture polysemy by introducing a hierarchical structure in the set of topics that the model can use to represent a given document. Our Gaussian hierarchical latent Dirichlet allocation significantly improves polysemy detection compared with Gaussian-based models and provides more parsimonious topic representations compared with hierarchical latent Dirichlet allocation. Our extensive quantitative experiments show that our model also achieves better topic coherence and held-out document predictive accuracy over a wide range of corpus and word embedding vectors.

ABSTRACT different granularities [3, 9] or use a cross interaction block that couple the features from different modalities [10, 6]. It is imperative that all modalities in multimodal interactions and 3. Fusion of unimodal and cross Therefore, to learn better cross modal information, we introduce 1.6% and 1.34% absolute improvement over current state-ofthe-art. Furthermore, to capture long term dependencies across 1. INTRODUCTION These are categorised into three types, 1. Methods that learn the modalities independently and fuse the In our proposed model, we aim to learn the interaction between [3, 4], and 3. Methods that explicitly learn contributions Personal use of this material is permitted. Multimodal sentiment analysis provides an opportunity to 2.1. M T V H T W H T V; W R d d (3) (U 1, U 2,..., U u) for a Text modality can be defined as: Cross attentive representations of Text (C V T R u d) and H T Bi-GRU(U 1, U 2,..., U u) (1) Video (C T V R u d) can be represented as: Subscript T denotes Text modality, A and V represent Audio As much as there is an opportunity to leverage cross modal interactions, representations is employed.

One promising approach to dealing with datapoints that are outside of the initial training distribution (OOD) is to create new classes that capture similarities in the datapoints previously rejected as uncategorizable. Systems that generate labels can be deployed against an arbitrary amount of data, discovering classification schemes that through training create a higher quality representation of data. We introduce the Dataset Reconstruction Accuracy, a new and important measure of the effectiveness of a model's ability to create labels. We introduce benchmarks against this Dataset Reconstruction metric. We apply a new heuristic, class learnability, for deciding whether a class is worthy of addition to the training dataset. We show that our class discovery system can be successfully applied to vision and language, and we demonstrate the value of semi-supervised learning in automatically discovering novel classes.

The goal of this study is to analyze the queries raised by customers on a particular social media platform by analyzing their interactions with the customer support and provide incisive insights to perform sentiment analysis. We performed exploratory data analysis to extract insights from the data. With Deep Learning tools like NLTK, sentiment analysis was performed to understand the positive, negative, and neutral sentiments of the customers of a brand. Machine Learning was used to identify the frequency of similar text appearances. Deep learning algorithms were used to understand the customer queries and the average time taken by the respective company's social customer support team in addressing the queries.

As a precursor to research about Sentiment Analysis with Text Classifiers (Naive Bayes, Maximum Entropy, SVM), Sentiment Analysis with bag-of-words was done and Positive / Negative Sentiment was detected with an accuracy of 60%. This is when only unigrams are used. This percentage will be much when bigrams or trigrams are used (in a next blog-post). See the results at: part 1: http://tinyurl.com/gnlfqqm