Applications that learn from opinionated documents, like tweets or product reviews, face two challenges. First, the opinionated documents constitute an evolving stream, where both the author's attitude and the vocabulary itself may change. Second, labels of documents are scarce and labels of words are unreliable, because the sentiment of a word depends on the (unknown) context in the author's mind. Most of the research on mining over opinionated streams focuses on the first aspect of the problem, whereas for the second a continuous supply of labels from the stream is assumed. Such an assumption though is utopian as the stream is infinite and the labeling cost is prohibitive. To this end, we investigate the potential of active stream learning algorithms that ask for labels on demand. Our proposed ACOSTREAM 1 approach works with limited labels: it uses an initial seed of labeled documents, occasionally requests additional labels for documents from the human expert and incrementally adapts to the underlying stream while exploiting the available labeled documents. In its core, ACOSTREAM consists of a MNB classifier coupled with "sampling" strategies for requesting class labels for new unlabeled documents. In the experiments, we evaluate the classifier performance over time by varying: (a) the class distribution of the opinionated stream, while assuming that the set of the words in the vocabulary is fixed but their polarities may change with the class distribution; and (b) the number of unknown words arriving at each moment, while the class polarity may also change. Our results show that active learning on a stream of opinionated documents, delivers good performance while requiring a small selection of labels
We skip a discussion on the most recent polarity classification algorithms such as @cite_24 as the contribution of our work is towards active learning strategies for polarity classification rather than pure polarity classification.
@cite_24
2101207956 2168816626 2963678405 2098653764
Polar codes, as the first provable capacity-achieving error-correcting codes, have received much attention in recent years. However, the decoding performance of polar codes with traditional successive-cancellation (SC) algorithm cannot match that of the low-density parity-check or Turbo codes. Because SC list (SCL) decoding algorithm can significantly improve the error-correcting performance of polar codes, design of SCL decoders is important for polar codes to be deployed in practical applications. However, because the prior latency reduction approaches for SC decoders are not applicable for SCL decoders, these list decoders suffer from the long-latency bottleneck. In this paper, we propose a multibit-decision approach that can significantly reduce latency of SCL decoders. First, we present a reformulated SCL algorithm that can perform intermediate decoding of 2 b together. The proposed approach, referred as 2-bit reformulated SCL ( 2b-rSCL ) algorithm , can reduce the latency of SCL decoder from ( @math ) to ( @math ) clock cycles without any performance loss. Then, we extend the idea of 2-b-decision to general case, and propose a general decoding scheme that can perform intermediate decoding of any @math bits simultaneously. This general approach, referred as @math -bit reformulated SCL ( @math b-rSCL ) algorithm , can reduce the overall decoding latency to as short as @math cycles. Furthermore, on the basis of the proposed algorithms, very large-scale integration architectures for 2b-rSCL and 4b-rSCL decoders are synthesized. Compared with a prior SCL decoder, the proposed (1024, 512) 2b-rSCL and 4b-rSCL decoders can achieve 21 and 60 reduction in latency, 1.66 and 2.77 times increase in coded throughput with list size 2, and 2.11 and 3.23 times increase in coded throughput with list size 4, respectively. Supervised polarity classification systems are typically domain-specific. Building these systems involves the expensive process of annotating a large amount of data for each domain. A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems. However, unsupervised learning of polarity is difficult, owing in part to the prevalence of sentimentally ambiguous reviews, where reviewers discuss both the positive and negative aspects of a product. To address this problem, we propose a semi-supervised approach to sentiment classification where we first mine the unambiguous reviews using spectral techniques and then exploit them to classify the ambiguous reviews via a novel combination of active learning, transductive learning, and ensemble learning. Long polar codes can achieve the symmetric capacity of arbitrary binary-input discrete memoryless channels under a low-complexity successive cancelation (SC) decoding algorithm. However, for polar codes with short and moderate code lengths, the decoding performance of the SC algorithm is inferior. The cyclic-redundancy-check (CRC)-aided SC-list (SCL)-decoding algorithm has better error performance than the SC algorithm for short or moderate polar codes. In this paper, we propose an efficient list decoder architecture for the CRC-aided SCL algorithm, based on both algorithmic reformulations and architectural techniques. In particular, an area efficient message memory architecture is proposed to reduce the area of the proposed decoder architecture. An efficient path pruning unit suitable for large list size is also proposed. For a polar code of length 1024 and rate 1 2, when list size @math and 4, the proposed list decoder architecture is implemented under a Taiwan Semiconductor Manufacturing Company (TSMC) 90-nm CMOS technology. Compared with the list decoders in the literature, our decoder achieves 1.24–1.83 times the area efficiency. In this paper, we propose a new class of lattices constructed from polar codes, namely polar lattices, to achieve the capacity @math of the additive white Gaussian-noise (AWGN) channel for any signal-to-noise ratio (SNR). Our construction follows the multilevel approach of , where on each level we construct a capacity-achieving polar code. The component polar codes are naturally nested, thereby fulfilling the requirement of the multilevel lattice construction. We prove that polar lattices are AWGN-good, in the sense that the error probability (for infinite lattice decoding) vanishes for any fixed volume-to-noise ratio (VNR) greater than @math . Furthermore, using the technique of source polarization, we propose discrete Gaussian shaping over the polar lattice to satisfy the power constraint. The proposed polar lattices permit low-complexity multistage successive cancellation decoding. Both the construction and shaping are explicit, and the overall complexity of encoding and decoding is @math for any fixed target error probability.