Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.

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Natural Language Processing (Almost) from Scratch

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

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The Graph Neural Network Model

If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate Brown clusters, Collobert and Weston (2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words on both NER and chunking. We use near state-of-the-art supervised baselines, and find that each of the three word representations improves the accuracy of these baselines. We find further improvements by combining different word representations. You can download our word features, for off-the-shelf use in existing NLP systems, as well as our code, here: http://metaoptimize.com/projects/wordreprs/

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Word Representations: A Simple and General Method for Semi-Supervised Learning

We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system. In the process of comparing several solutions to these challenges we reach some surprising conclusions, as well as develop an NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task, the best reported result for this dataset.

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Design Challenges and Misconceptions in Named Entity Recognition

PROBLEM TO BE SOLVED: To provide a device for converting a first word belonging to a certain language system into a second word corresponding to the other language system. SOLUTION: A word translation device 5B is provided with: a word output part 5 for preparing a database by referring to a transliteration probability model 7, and using approximation under the consideration of a word set maximizing conditioned probability with the character history of the word set as conditions, and for retrieving a second word corresponding to an input first word; a conversion candidate retrieving part 40 for outputting a third word extracted from document data acquired by electronic equipment 50 connected to a communication network NW based on the first word as a conversion candidate to the second word; and a conversion possibility calculating part 30 for calculating status transition weight based on the third word acquired by the conversion candidate retrieval part 40 by referring to a composite database acquired by compounding the history relating to characters comprising the third word with the database prepared by the word output part 5. COPYRIGHT: (C)2007,JPO&INPIT

Symbol string conversion method, word translation method, its device, its program and recording medium

Conventional sentence compression methods employ a syntactic parser to compress a sentence without changing its meaning. However, the reference compressions made by humans do not always retain the syntactic structures of the original sentences. Moreover, for the goal of on-demand sentence compression, the time spent in the parsing stage is not negligible. As an alternative to syntactic parsing, we propose a novel term weighting technique based on the positional information within the original sentence and a novel language model that combines statistics from the original sentence and a general corpus. Experiments that involve both human subjective evaluations and automatic evaluations show that our method outperforms Hori's method, a state-of-the-art conventional technique. Because our method does not use a syntactic parser, it is 4.3 times faster than Hori's method.

A Syntax-Free Approach to Japanese Sentence Compression

Discriminative named entity recognition of speech data using speech recognition confidence.

Our new research project called "ambient intelligence" concentrates on the creation of new lifestyles through research on communication science and intelligence integration. It is premised on the creation of such virtual communication partners as fairies and goblins that can be constantly at our side. We call these virtual communication partners mushrooms.To show the essence of ambient intelligence, we developed two multimodal prototype systems: mushrooms that watch, listen, and answer questions and a Quizmaster Mushroom. These two systems work in real time using speech, sound, dialogue, and vision technologies.We performed preliminary experiments with the Quizmaster Mushroom. The results showed that the system can transmit knowledge to users while they are playing the quizzes.Furthermore, through the two mushrooms, we found policies for design effects in multimodal interface and integration.

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The world of mushrooms: human-computer interaction prototype systems for ambient intelligence

In order to promote the study of automatic summarization and translation, we need an accurate automatic evaluation method that is close to human evaluation. In this paper, we present an evaluation method that is based on convolution kernels that measure the similarities between texts considering their substructures. We conducted an experiment using automatic summarization evaluation data developed for Text Summarization Challenge 3 (TSC-3). A comparison with conventional techniques shows that our method correlates more closely with human evaluations and is more robust.

Hideki Isozaki

Papers

Symbol string conversion method, word translation method, its device, its program and recording medium

A Syntax-Free Approach to Japanese Sentence Compression

Discriminative named entity recognition of speech data using speech recognition confidence.

The world of mushrooms: human-computer interaction prototype systems for ambient intelligence

Kernel-based Approach for Automatic Evaluation of Natural Language Generation Technologies: Application to Automatic Summarization