Regular expressions are an important building block of rule-based information extraction systems. Regexes can encode rules to recognize instances of simple entities which can then feed into the identification of more complex cross-entity relationships. Manually crafting a regex that recognizes all possible instances of an entity is difficult since an entity can manifest in a variety of different forms. Thus, the problem of automatically generalizing manually crafted seed regexes to improve the recall of IE systems has attracted research attention. In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex. Our approach starts from a manually authored high precision seed regex for the entity of interest, and uses the matches of the seed regex and the context around these matches to identify more instances of the entity. These are then used to identify a set of diverse, high recall regexes that are representative of this entity. Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach.

For document level sentiment analysis (SA), Subjectivity Extraction, ie., extracting the relevant subjective portions of the text that cover the overall sentiment expressed in the document, is an important step. Subjectivity Extraction, however, is a hard problem for systems, as it demands a great deal of world knowledge and reasoning. Humans, on the other hand, are good at extracting relevant subjective summaries from an opinionated document (say, a movie review), while inferring the sentiment expressed in it. This capability is manifested in their eye-movement behavior while reading: words pertaining to the subjective summary of the text attract a lot more attention in the form of gaze-fixations and/or saccadic patterns. We propose a multi-task deep neural framework for document level sentiment analysis that learns to predict the overall sentiment expressed in the given input document, by simultaneously learning to predict human gaze behavior and auxiliary linguistic tasks like part-of-speech and syntactic properties of words in the document. For this, a multi-task learning algorithm based on multi-layer shared LSTM augmented with task specific classifiers is proposed. With this composite multi-task network, we obtain performance competitive with or better than state-of-the-art approaches in SA. Moreover, the availability of gaze predictions as an auxiliary output helps interpret the system better; for instance, gaze predictions reveal that the system indeed performs subjectivity extraction better, which accounts for improvement in document level sentiment analysis performance.

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classification in a target domain that has no or little supervised information. Existing cross-domain sentiment classification methods cannot automatically capture non-pivots, i.e., the domain-specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The proposed HATN provides a hierarchical attention transfer mechanism which can transfer attentions for emotions across domains by automatically capturing pivots and non-pivots. Besides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net aligning the non-pivots by using the pivots as a bridge. Specifically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P-net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.

To understand narrative text, we must comprehend how people are affected by the events that they experience. For example, readers understand that graduating from college is a positive event (achievement) but being fired from one's job is a negative event (problem). NLP researchers have developed effective tools for recognizing explicit sentiments, but affective events are more difficult to recognize because the polarity is often implicit and can depend on both a predicate and its arguments. Our research investigates the prevalence of affective events in a personal story corpus, and introduces a weakly supervised method for large scale induction of affective events. We present an iterative learning framework that constructs a graph with nodes representing events and initializes their affective polarities with sentiment analysis tools as weak supervision. The events are then linked based on three types of semantic relations: (1) semantic similarity, (2) semantic opposition, and (3) shared components. The learning algorithm iteratively refines the polarity values by optimizing semantic consistency across all events in the graph. Our model learns over 100,000 affective events and identifies their polarities more accurately than other methods.

Aspect-level sentiment classification aims at detecting the sentiment expressed towards a particular target in a sentence. Based on the observation that the sentiment polarity is often related to specific spans in the given sentence, it is possible to make use of such information for better classification. On the other hand, such information can also serve as justifications associated with the predictions.We propose a segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer. The model simulates human's process of inferring sentiment information when reading: when given a target, humans tend to search for surrounding relevant text spans in the sentence before making an informed decision on the underlying sentiment information.We perform sentiment classification tasks on publicly available datasets on online reviews across different languages from SemEval tasks and social comments from Twitter. Extensive experiments show that our model achieves the state-of-the-art performance while extracting interpretable sentiment expressions.  

Context-based word embedding learning approaches can model rich semantic and syntactic information. However, it is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarities, such as good and bad, are mapped into close word vectors in the embedding space. Recently, some sentiment embedding learning methods have been proposed, but most of them are designed to work well on sentence-level texts. Directly applying those models to document-level texts often leads to unsatisfied results. To address this issue, we present a sentiment-specific word embedding learning architecture that utilizes local context informationas well as global sentiment representation. The architecture is applicable for both sentence-level and document-level texts. We take global sentiment representation as a simple average of word embeddings in the text, and use a corruption strategy as a sentiment-dependent regularization. Extensive experiments conducted on several benchmark datasets demonstrate that the proposed architecture outperforms the state-of-the-art methods for sentiment classification.

With the recent development of deep learning, research in AI has gained new vigor and prominence. While machine learning has succeeded in revitalizing many research fields, such as computer vision, speech recognition, and medical diagnosis, we are yet to witness impressive progress in natural language understanding. One of the reasons behind this unmatched expectation is that, while a bottom-up approach is feasible for pattern recognition, reasoning and understanding often require a top-down approach. In this work, we couple sub-symbolic and symbolic AI to automatically discover conceptual primitives from text and link them to commonsense concepts and named entities in a new three-level knowledge representation for sentiment analysis. In particular, we employ recurrent neural networks to infer primitives by lexical substitution and use them for grounding common and commonsense knowledge by means of multi-dimensional scaling.

Crowdsourcing technique provides an efficient platform to employ human skills in sentiment analysis, which is a difficult task for automatic language models due to the large variations in context, writing style, view point and so on. However, the standard crowdsourcing aggregation models are incompetent when the number of crowd labels per worker is not sufficient to train parameters, or when it is not feasible to collect labels for each sample in a large dataset. In this paper, we propose a novel hybrid model to exploit both crowd and text data for sentiment analysis, consisting of a generative crowdsourcing aggregation model and a deep sentimental autoencoder. Combination of these two sub-models is obtained based on a probabilistic framework rather than a heuristic way. We introduce a unified objective function to incorporate the objectives of both sub-models, and derive an efficient optimization algorithm to jointly solve the corresponding problem. Experimental results indicate that our model achieves superior results in comparison with the state-of-the-art models, especially when the crowd labels are scarce.

As we enter 2018 it's become clear that the jobs landscape in the United States is changing. How people are thinking about their careers and how they define success is changing. The rise of technology across every industry has created a flurry of new jobs and associated skills (and these aren't necessarily all tech roles). While we all may take a different approach to reach our own definition of success, we've compiled a list of the most promising jobs and in-demand skills, plus a few stand-out trends, to help you get there. You don't need to be technical to be successful.

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.