Probabilistic modeling is one of the foundations of modern Learning the structure of a probabilistic model resembles machine learning and artificial intelligence, which aims to learning the set of production rules of a grammar, while compactly represent the joint probability distribution of random learning model parameters resembles learning grammar rule variables. The most widely used approach for probabilistic probabilities. From the unsupervised grammar learning literature, modeling is probabilistic graphical models. A probabilistic one can see that learning approaches based on PCFGs graphical model represents a probability distribution with a have not been very successful, while the state-of-the-art performance directed or undirected graph. It represents random variables has mostly been achieved based on less expressive with the nodes in the graph and uses the edges in the graph to models such as dependency grammars (DGs) (Klein and encode the probabilistic relationships between random variables.

In this paper we propose a simple yet efficient method for constrained word deletion to compress sentences, based on top-down greedy local flipping from multiple random initializations. The algorithm naturally integrates various grammatical constraints in the compression process, without using time-consuming integer linear programming solvers. Our formulation suits for any objective function involving arbitrary local score definition. Experimental results show that the proposed method achieves nearly identical performance with explicit ILP formulation while being much more efficient.

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.

The sequence-to-sequence model is proven to be extremely successful in constituent parsing. It relies on one key technique, the probabilistic attention mechanism, to automatically select the context for prediction. Despite its successes, the probabilistic attention model does not always select the most important context. For example, the headword and boundary words of a subtree have been shown to be critical when predicting the constituent label of the subtree, but this contextual information becomes increasingly difficult to learn as the length of the sequence increases. In this study, we proposed a deterministic attention mechanism that deterministically selects the important context and is not affected by the sequence length. We implemented two different instances of this framework. When combined with a novel bottom-up linearization method, our parser demonstrated better performance than that achieved by the sequence-to-sequence parser with probabilistic attention mechanism.

Traditional semantic parsers map language onto compositional, executable queries in a fixed schema. This mapping allows them to effectively leverage the information contained in large, formal knowledge bases (KBs, e.g., Freebase) to answer questions, but it is also fundamentally limiting---these semantic parsers can only assign meaning to language that falls within the KB's manually-produced schema. Recently proposed methods for open vocabulary semantic parsing overcome this limitation by learning execution models for arbitrary language, essentially using a text corpus as a kind of knowledge base. However, all prior approaches to open vocabulary semantic parsing replace a formal KB with textual information, making no use of the KB in their models. We show how to combine the disparate representations used by these two approaches, presenting for the first time a semantic parser that (1) produces compositional, executable representations of language, (2) can successfully leverage the information contained in both a formal KB and a large corpus, and (3) is not limited to the schema of the underlying KB. We demonstrate significantly improved performance over state-of-the-art baselines on an open-domain natural language question answering task.

Understanding inter-character relationships is fundamental for understanding character intentions and goals in a narrative. This paper addresses unsupervised modeling of relationships between characters. We model relationships as dynamic phenomenon, represented as evolving sequences of latent states empirically learned from data. Unlike most previous work our approach is completely unsupervised. This enables data-driven inference of inter-character relationship types beyond simple sentiment polarities, by incorporating lexical and semantic representations, and leveraging large quantities of raw text. We present three models based on rich sets of linguistic features that capture various cues about relationships. We compare these models with existing techniques and also demonstrate that relationship categories learned by our model are semantically coherent.

In this paper, we tackle the problem of segmenting out a sequence of actions from videos. The videos contain background and actions which are usually composed of ordered sub-actions. We refer the sub-actions and the background as semantic units. Considering the possible overlap between two adjacent semantic units, we utilize label distributions to annotate the various segments in the video. The label distribution covers a certain number of semantic unit labels, representing the degree to which each label describes the video segment. The mapping from a video segment to its label distribution is then learned by a Label Distribution Learning (LDL) algorithm. Based on the LDL model, a soft video parsing method with segmental regular grammars is proposed to construct a tree structure for the video. Each leaf of the tree stands for a video clip of background or sub-action. The proposed method shows promising results on the THUMOS'14 and MSR-II datasets and its computational complexity is much less than the state-of-the-art method.

In much of machine learning, data used for training and inference undergoes a preprocessing step, where multiple inputs (such as images) are scaled to the same dimensions and stacked into batches. This lets high-performance deep learning libraries like TensorFlow run the same computation graph across all the inputs in the batch in parallel. Batching exploits the SIMD capabilities of modern GPUs and multi-core CPUs to speed up execution. However, there are many problem domains where the size and structure of the input data varies, such as parse trees in natural language understanding, abstract syntax trees in source code, DOM trees for web pages and more. In these cases, the different inputs have different computation graphs that don't naturally batch together, resulting in poor processor, memory, and cache utilization. Today we are releasing TensorFlow Fold to address these challenges.

Currently two barriers exist that sabotage clothing semantic parsing research: existing methods are time-consuming and the lack of large publicly available dataset that enables parsing at multiple scales. To mitigate these two dilemmas, we hereby embrace deep learning method and design a lightweight multi-scale inception neural network which is at both inside and outside multi-scale inception during training. Moreover, atrous convolution block is involved to enlarge the field of view while bringing neither extra computation cost nor parameters. Then the pre-trained model is further pruned and compressed by fine-tuning on a lightweight version of the same network used earlier, in which the inactive feature response and connections below a pre-defined threshold are directly removed. Besides, we construct so far the largest fashion guided clothing semantic parsing dataset (FCP) which contains a total of 5,000 clothing images and each image associates with both pixel-level, object-level and image-level annotations. All clothing in the dataset are recommended by fashion experts or trendsetters and contains as many as 65 common clothing items, accessories. We organize the dataset as Wordnet tree structure so that it enables fashionably parsing hierarchically. Finally, we conduct extensive experiments on three currently available datasets. Both quantitative and qualitative results demonstrate the priority and feasibility of our method, comparing with several other deep learning based methods. Our method achieves 35 FPS in a single Nvidia Titian X GPU with only minimal accuracy loss.

That model is fairly slow. Essentially, that model is trying to pull out all stops to maximize tagger accuracy. Speed consequently suffers due to choices like using 4th order bidirectional tag conditioning. It's nearly as accurate (96.97% accuracy vs. 97.32% on the standard WSJ22-24 test set) and is an order of magnitude faster. Compared to MXPOST, the Stanford POS Tagger with this model is both more accurate and considerably faster. It all depends, but on a 2008 nothing-special Intel server, it tags about 15000 words per second.