Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence.This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms.While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus.In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level.Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables.Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.

Aspect-level sentiment classification aims to determine the sentiment polarity of a review sentence towards an opinion target. A sentence could contain multiple sentiment-target pairs; thus the main challenge of this task is to separate different opinion contexts for different targets. To this end, attention mechanism has played an important role in previous state-of-the-art neural models. The mechanism is able to capture the importance of each context word towards a target by modeling their semantic associations. We build upon this line of research and propose two novel approaches for improving the effectiveness of attention. First, we propose a method for target representation that better captures the semantic meaning of the opinion target. Second, we introduce an attention model that incorporates syntactic information into the attention mechanism. We experiment on attention-based LSTM (Long Short-Term Memory) models using the datasets from SemEval 2014, 2015, and 2016. The experimental results show that the conventional attention-based LSTM can be substantially improved by incorporating the two approaches.

We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain.Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space.With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations -entropy minimization and self-ensemble bootstrapping -to incorporate the unlabeled target data for classifier refinement.Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multi-layer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves state-of-the-art performances compared with several baselines.

Aspect extraction is an important and challenging task in aspect-based sentiment analysis.Existing works tend to apply variants of topic models on this task.While fairly successful, these methods usually do not produce highly coherent aspects.In this paper, we present a novel neural approach with the aim of discovering coherent aspects.The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings.Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space.In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects.Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks.

Driven by the rapidly increasing demand for handling real-time data streams, many data stream processing (DSP) systems have been proposed. Regardless of the different architectures of those DSP systems, they are mostly aiming at scaling out using a cluster of commodity machines and built around a number of key design aspects: a) pipelined processing with message passing, b) on-demand data parallelism, and c) JVM based implementation. However, there lacks a study on those key design aspects on modern scale-up architectures, where more CPU cores are being put on the same die, and the onchip cache hierarchies are getting larger, deeper, and complex. Multiple sockets bring non-uniform memory access (NUMA) effort. In this paper, we revisit the aforementioned design aspects on a modern scale-up server. Specifically, we use a series of applications as micro benchmark to conduct detailed profiling studies on Apache Storm and Flink. From the profiling results, we observe two major performance issues: a) the massively parallel execution model causes serious front-end stalls, which are a major performance bottleneck issue on a single CPU socket, b) the lack of NUMA-aware mechanism causes major drawback on the scalability of DSP systems on multi-socket architectures. Addressing these issues should allow DSP systems to exploit modern scale-up architectures, which also benefits scaling out environments. We present our initial efforts on resolving the above-mentioned performance issues, which have shown up to 3.2x and 3.1x improvement on the performance of Storm and Flink, respectively.

SAP Event Stream Processor (ESP) platform aims at delivering real-time stream processing and analytics in many time-critical areas such as Capital Markets, Internet of Things (IoT) and Data Center Intelligence. SAP ESP allows users to realize complex event processing (CEP) in the form of pattern queries. In this paper, we present MOTTO - a multi-query optimizer in SAP ESP in order to improve the performance of many concurrent pattern queries. This is motivated by the observations that many real-world applications usually have concurrent pattern queries working on the same data streams, leading to tremendous sharing opportunities among queries. In MOTTO, we leverage three major sharing techniques, namely merge, decomposition and operator transformation sharing, to reduce redundant computation among pattern queries. In addition, MOTTO supports nested pattern queries as well as pattern queries with different window sizes. The experiments demonstrate the efficiency of the MOTTO with real-world application scenarios and sensitivity studies.

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks.Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task.In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction.The proposed model learns high-level discriminative features and double propagates information between aspect and opinion terms, simultaneously.Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance.Experimental results on the dataset from SemEval Challenge 2014 task 4 show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.

MOTIVATION: Semantic role labeling (SRL) is a natural language processing (NLP) task that extracts a shallow meaning representation from free text sentences. Several efforts to create SRL systems for the biomedical domain have been made during the last few years. However, state-of-the-art SRL relies on manually annotated training instances, which are rare and expensive to prepare. In this article, we address SRL for the biomedical domain as a domain adaptation problem to leverage existing SRL resources from the newswire domain. RESULTS: We evaluate the performance of three recently proposed domain adaptation algorithms for SRL. Our results show that by using domain adaptation, the cost of developing an SRL system for the biomedical domain can be reduced significantly. Using domain adaptation, our system can achieve 97% of the performance with as little as 60 annotated target domain abstracts. AVAILABILITY: Our BioKIT system that performs SRL in the biomedical domain as described in this article is implemented in Python and C and operates under the Linux operating system. BioKIT can be downloaded at http://nlp.comp.nus.edu.sg/software. The domain adaptation software is available for download at http://www.mysmu.edu/faculty/jingjiang/software/DALR.html. The BioProp corpus is available from the Linguistic Data Consortium http://www.ldc.upenn.edu.