Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.Comment: To be published in EMNLP 201

Partial-Candidate Commit for Chinese Pinyin Text Entry

This publication describes systems and techniques directed to committing a partial candidate for a coding-style language. Key codes representing a coding-style language are input through a user interface to a computing device. In one aspect, the key codes may be pinyin text for translation to an output-language of Chinese characters. The computing device generates output-language candidates that are representative of the key codes. An output-language candidate is identified that represents an intended communication relative to the key codes. A portion of the identified output-language candidate is selected to commit to the intended communication. This partial selection of the output-language candidate is completed through the user interface (e.g., a swipe gesture, a tap gesture). The user interface commits to acceptance only that partial selection of the output-language candidate for the intended communication

TWO-HANDED TYPING METHOD ON AN ARBITRARY SURFACE

A computing device may detect user input, such as finger movements resembling typing on an invisible virtual keyboard in the air or on any surface, to enable typing. The computing device may use sensors (e.g., accelerometers, cameras, piezoelectric sensors, etc.) to detect the user’s finger movements, such as the user’s fingers moving through the air and/or contacting a surface. The computing device may then decode (or, in other words, convert, interpret, analyze, etc.) the detected finger movements to identify corresponding inputs representative of characters (e.g., alphanumeric characters, national characters, special characters, etc.). To reduce input errors, the computing device may decode the detected finger movements, at least in part, based on contextual information, such as preceding characters, words, and/or the like entered via previously detected user inputs. Similarly, the computing device may apply machine learning techniques and adjust parameters, such as a signal-to-noise ratio, to improve the accuracy of input-entry. In some examples, the computing device may implement specific recognition, prediction, and correction algorithms to improve the accuracy of input-entry. In this way, the computing device may accommodate biasing in finger movements that may be specific to a user entering the input

Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks

We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available. Inspired by the rich semantic correlations between classes at the long tail and those at the head, we take advantage of the knowledge from data-rich classes at the head of the distribution to boost the performance of the data-poor classes at the tail. First, we propose to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks. Second, we integrate that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism. We demonstrate our results for a large-scale benchmark dataset which show that our approach significantly outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201

General Trinajstić Index

In memory of the outstanding theoretical chemist Nenad Trinajstić, Furtula introduced a new distance-based molecular structure descriptor "Trinajstić index" in chemical graph theory. In this paper, we propose the general Trinajstić index, and give the calculation formula of the general Trinajstić index for double-star graphs, double brooms, Kragujevac trees, firefly graphs and wheel graphs. As an application, we calculate the general Trinajstić index for some hydrocarbons