Eric Nichols

The hinoki treebank a treebank for text understanding

In this paper we describe the motivation for and construction of a new Japanese lexical resource: the Hinoki treebank. The treebank is built from dictionary definition sentences, and uses an HPSG grammar to encode the syntactic and semantic information. We then show how this treebank can be used to extract thesaurus information from definition sentences in a language-neutral way using minimal recursion semantics.

Statement map: assisting information crediblity analysis by visualizing arguments

In this paper we introduce Statement Map, a project designed to help users navigate the vast amounts of information on the internet and come to informed opinions on topics of interest. It does this by mining the Web for a variety of viewpoints and presenting them to users together with supporting evidence in a way that makes it clear how the viewpoints are related. In this paper, we discuss the need to address issues of information credibility on the internet, outline the development of Statement Map generators for Japanese and English, discuss the technical issues that are being addressed, and report on the construction of the resources necessary to meet the projects goals.

Acquiring an ontology for a fundamental vocabulary

In this paper we describe the extraction of thesaurus information from parsed dictionary definition sentences. The main data for our experiments comes from Lexeed, a Japanese semantic dictionary, and the Hinoki treebank built on it. The dictionary is parsed using a head-driven phrase structure grammar of Japanese. Knowledge is extracted from the semantic representation (Minimal Recursion Semantics). This makes the extraction process language independent.

Statement map: reducing web information credibility noise through opinion classification

On the Internet, users often encounter noise in the form of spelling errors or unknown words, however, dishonest, unreliable, or biased information also acts as noise that makes it difficult to find credible sources of information. As people come to rely on the Internet for more and more information, reducing this credibility noise grows ever more urgent. The STATEMENT MAP projects goal is to help Internet users evaluate the credibility of information sources by mining the Web for a variety of viewpoints on their topics of interest and presenting them to users together with supporting evidence in a way that makes it clear how they are related. In this paper, we show how a STATEMENT MAP system can be constructed by combining Information Retrieval (IR) and Natural Language Processing (NLP) technologies, focusing on the task of organizing statements retrieved from the Web by viewpoints. We frame this as a semantic relation classification task, and identify 4 semantic relations: [AGREEMENT], [CONFLICT], [CONFINEMENT], and [EVIDENCE]. The former two relations are identified by measuring semantic similarity through sentence alignment, while the latter two are identified through sentence-internal discourse processing. As a prelude to end-to-end user evaluation of STATEMENT MAP, we present a large-scale evaluation of semantic relation classification between user queries and Internet texts in Japanese and conduct detailed error analysis to identify the remaining areas of improvement.

Deep open-source machine translation

This paper summarizes ongoing efforts to provide software infrastructure (and methodology) for open-source machine translation that combines a deep semantic transfer approach with advanced stochastic models. The resulting infrastructure combines precise grammars for parsing and generation, a semantic-transfer based translation engine and stochastic controllers. We provide both a qualitative and quantitative experience report from instantiating our general architecture for Japanese–English MT using only open-source components, including HPSG-based grammars of English and Japanese.

Leveraging Diverse Lexical Resources for Textual Entailment Recognition

Since the problem of textual entailment recognition requires capturing semantic relations between diverse expressions of language, linguistic and world knowledge play an important role. In this article, we explore the effectiveness of different types of currently available resources including synonyms, antonyms, hypernym-hyponym relations, and lexical entailment relations for the task of textual entailment recognition. In order to do so, we develop an entailment relation recognition system which utilizes diverse linguistic analyses and resources to align the linguistic units in a pair of texts and identifies entailment relations based on these alignments. We use the Japanese subset of the NTCIR-9 RITE-1 dataset for evaluation and error analysis, conducting ablation testing and evaluation on hand-crafted alignment gold standard data to evaluate the contribution of individual resources. Error analysis shows that existing knowledge sources are effective for RTE, but that their coverage is limited, especially for domain-specific and other low-frequency expressions. To increase alignment coverage on such expressions, we propose a method of alignment inference that uses syntactic and semantic dependency information to identify likely alignments without relying on external resources. Evaluation adding alignment inference to a system using all available knowledge sources shows improvements in both precision and recall of entailment relation recognition.

An Attention-based Regression Model for Grounding Textual Phrases in Images

Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.

Organizing Information on the Web through Agreement-Conflict Relation Classification

The vast amount of information on the Web makes it difficult for users to comprehensively survey the various viewpoints on topics of interest. To help users cope with this information overload, we have developed an Information Organization System that applies state-of-the-art technology from Recognizing Textual Entailment to automatically detect Web texts that are relevant to natural language queries and organize them into agreeing and conflicting groups. Users are presented with a bird’s-eye-view visualization of the viewpoints on their queries that makes it easier to gain a deeper understanding of an issue. In this paper, we describe the implementation of our Information Organization System and evaluate our system through empirical analysis of the semantic relation recognition system that classifies texts and through a large-scale usability study. The empirical evaluation and usability study both demonstrate the usefulness of our system. User feedback further shows that by exposing our users to differing viewpoints promotes objective thinking and helps to reduce confirmation bias.