The cultural origins of human cognition

Hand and Mind: What Gestures Reveal about Thought. David McNeill. Chicago and London: University of Chicago Press, 1992. 416 pp.

Hand and Mind: What Gestures Reveal about Thought

Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to many applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.

A Survey of the Usages of Deep Learning for Natural Language Processing

The CoNLL-2012 shared task involved predicting coreference in three languages -- English, Chinese and Arabic -- using OntoNotes data. It was a follow-on to the English-only task organized in 2011. Until the creation of the OntoNotes corpus, resources in this subfield of language processing have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types and covering multiple languages. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating systems. Being a task that has a complex evaluation history, and multiple evalation conditions, it has, in the past, been difficult to judge the improvement in new algorithms over previously reported results. Having a standard test set and evaluation parameters, all based on a resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.

CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

For the 11th straight year, the Conference on Computational Natural Language Learning has been accompanied by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2009, the shared task was dedicated to the joint parsing of syntactic and semantic dependencies in multiple languages. This shared task combines the shared tasks of the previous five years under a unique dependency-based formalism similar to the 2008 task. In this paper, we define the shared task, describe how the data sets were created and show their quantitative properties, report the results and summarize the approaches of the participating systems.

/pdf/the-conll-2009-shared-task-syntactic-and-semantic-qnvu98twhw.pdf

The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages

We present an integrated probabilistic model for Japanese syntactic and case structure analysis. Syntactic and case structure are simultaneously analyzed based on wide-coverage case frames that are constructed from a huge raw corpus in an unsupervised manner. This model selects the syntactic and case structure that has the highest generative probability. We evaluate both syntactic structure and case structure. In particular, the experimental results for syntactic analysis on web sentences show that the proposed model significantly outperforms known syntactic analyzers.

/pdf/a-fully-lexicalized-probabilistic-model-for-japanese-2mnn178n2l.pdf

A Fully-Lexicalized Probabilistic Model for Japanese Syntactic and Case Structure Analysis

Case frames are important knowledge for a variety of NLP systems, especially when wide-coverage case frames are available. To acquire such large-scale case frames, it is necessary to automatically compile them from an enormous amount of corpus. In this paper, we consider the web as a corpus. We first build a huge text corpus from the web, and then construct case frames from the corpus. It is infeasible to do these processes by one CPU, and thus we employ a high-performance computing environment composed of 350 CPUs. The acquired corpus consists of 470M sentences, and the case frames compiled from them have 90,000 verb entries. The case frames contain most examples of usual use, and are ready to be applied to lots of NLP analyses and applications.

https://nlp.ist.i.kyoto-u.ac.jp/member/kawahara/paper/NL-171.pdf

Case Frame Compilation from the Web using High-Performance Computing

本稿では, 格フレームに基づき構文・格解析を統合的に行う確率モデルを提案する.格フレームは, ウェブテキスト約5億文から自動的に構築した大規模なものを用いる.確率モデルは, 述語項構造を基本単位とし, それを生成する確率であり, 格フレームによる語彙的な選好を利用するものである.ウェブのテキストを用いて実験を行い, 特に述語項構造に関連する係り受けの精度が向上することを確認した.また, 語彙的選好がどの程度用いられているかを調査したところ, 60.7%という高い割合で使われていることがわかり, カバレージの高さを確認することができた.

We present a new morphological analysis model that considers semantic plausibility of word sequences by using a recurrent neural network language model (RNNLM). In unsegmented languages, since language models are learned from automatically segmented texts and inevitably contain errors, it is not apparent that conventional language models contribute to morphological analysis. To solve this problem, we do not use language models based on raw word sequences but use a semantically generalized language model, RNNLM, in morphological analysis. In our experiments on two Japanese corpora, our proposed model significantly outperformed baseline models. This result indicates the effectiveness of RNNLM in morphological analysis.

/pdf/morphological-analysis-for-unsegmented-languages-using-3iywey2m7i.pdf

Daisuke Kawahara

Papers

The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages

A Fully-Lexicalized Probabilistic Model for Japanese Syntactic and Case Structure Analysis

Case Frame Compilation from the Web using High-Performance Computing

A Fully-Lexicalized Probabilistic Model for Japanese Syntactic and Case Structure Analysis

Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model