Hisami Suzuki

Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion

Abstract In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.

Acquiring ontological knowledge from query logs

We present a method for acquiring ontological knowledge using search query logs. We first use query logs to identify important contexts associated with terms belonging to a semantic category; we then use these contexts to harvest new words belonging to this category. Our evaluation on selected categories indicates that the method works very well to help harvesting terms, achieving 85% to 95% accuracy in categorizing newly acquired terms.

MindNet: An Automatically-Created Lexical Resource

We will demonstrate MindNet, a lexical resource built automatically by processing text. We will present two forms of MindNet: as a static lexical resource, and, as a toolkit which allows MindNets to be built from arbitrary text. We will also introduce a web-based interface to MindNet lexicons (MNEX) that is intended to make the data contained within MindNets more accessible for exploration. Both English and Japanese MindNets will be shown and will be made available, through MNEX, for research purposes.

Unsupervised Learning of Dependency Structure for Language Modeling

This paper presents a dependency language model (DLM) that captures linguistic constraints via a dependency structure, i.e., a set of probabilistic dependencies that express the relations between headwords of each phrase in a sentence by an acyclic, planar, undirected graph. Our contributions are three-fold. First, we incorporate the dependency structure into an n-gram language model to capture long distance word dependency. Second, we present an unsupervised learning method that discovers the dependency structure of a sentence using a bootstrapping procedure. Finally, we evaluate the proposed models on a realistic application (Japanese Kana-Kanji conversion). Experiments show that the best DLM achieves an 11.3% error rate reduction over the word trigram model.

Learning to Predict Case Markers in Japanese

Japanese case markers, which indicate the grammatical relation of the complement NP to the predicate, often pose challenges to the generation of Japanese text, be it done by a foreign language learner, or by a machine translation (MT) system. In this paper, we describe the task of predicting Japanese case markers and propose machine learning methods for solving it in two settings: (i) monolingual, when given information only from the Japanese sentence; and (ii) bilingual, when also given information from a corresponding English source sentence in an MT context. We formulate the task after the well-studied task of English semantic role labelling, and explore features from a syntactic dependency structure of the sentence. For the monolingual task, we evaluated our models on the Kyoto Corpus and achieved over 84% accuracy in assigning correct case markers for each phrase. For the bilingual task, we achieved an accuracy of 92% per phrase using a bilingual dataset from a technical domain. We show that in both settings, features that exploit dependency information, whether derived from gold-standard annotations or automatically assigned, contribute significantly to the prediction of case markers.

Exploiting Headword Dependency and Predictive Clustering for Language Modeling

This paper presents several practical ways of incorporating linguistic structure into language models. A headword detector is first applied to detect the headword of each phrase in a sentence. A permuted headword trigram model (PHTM) is then generated from the annotated corpus. Finally, PHTM is extended to a cluster PHTM (C-PHTM) by defining clusters for similar words in the corpus. We evaluated the proposed models on the realistic application of Japanese Kana-Kanji conversion. Experiments show that C-PHTM achieves 15% error rate reduction over the word trigram model. This demonstrates that the use of simple methods such as the headword trigram and predictive clustering can effectively capture long distance word dependency, and substantially outperform a word trigram model.

A Comparative Study on Language Model Adaptation Techniques Using New Evaluation Metrics

This paper presents comparative experimental results on four techniques of language model adaptation, including a maximum a posteriori (MAP) method and three discriminative training methods, the boosting algorithm, the average perceptron and the minimum sample risk method, on the task of Japanese Kana-Kanji conversion. We evaluate these techniques beyond simply using the character error rate (CER): the CER results are interpreted using a metric of domain similarity between background and adaptation domains, and are further evaluated by correlating them with a novel metric for measuring the side effects of adapted models. Using these metrics, we show that the discriminative methods are superior to a MAP-based method not only in terms of achieving larger CER reduction, but also of being more robust against the similarity of background and adaptation domains, and achieve larger CER reduction with fewer side effects.

An empirical study on language model adaptation

This article presents an empirical study of four techniques for adapting language models, including a maximum a posteriori (MAP) method and three discriminative training models, in the application of Japanese Kana-Kanji conversion. We compare the performance of these methods from various angles by adapting the baseline model to four adaptation domains. In particular, we attempt to interpret the results in terms of the character error rate (CER) by correlating them with the characteristics of the adaptation domain, measured by using the information-theoretic notion of cross entropy. We show that such a metric correlates well with the CER performance of the adaptation methods, and also show that the discriminative methods are not only superior to a MAP-based method in achieving larger CER reduction, but also in having fewer side effects and being more robust against the similarity between background and adaptation domains.

An overview of end-to-end language understanding and dialog management for personal digital assistants

Spoken language understanding and dialog management have emerged as key technologies in interacting with personal digital assistants (PDAs). The coverage, complexity, and the scale of PDAs are much larger than previous conversational understanding systems. As such, new problems arise. In this paper, we provide an overview of the language understanding and dialog management capabilities of PDAs, focusing particularly on Cortana, Microsofts PDA. We explain the system architecture for language understanding and dialog management for our PDA, indicate how it differs with prior state-of-the-art systems, and describe key components. We also report a set of experiments detailing system performance on a variety of scenarios and tasks. We describe how the quality of user experiences are measured end-to-end and also discuss open issues.

Discriminative Substring Decoding for Transliteration

We present a discriminative substring decoder for transliteration. This decoder extends recent approaches for discriminative character transduction by allowing for a list of known target-language words, an important resource for transliteration. Our approach improves upon Sherif and Kondraks (2007b) state-of-the-art decoder, creating a 28.5% relative improvement in transliteration accuracy on a Japanese katakana-to-English task. We also conduct a controlled comparison of two feature paradigms for discriminative training: indicators and hybrid generative features. Surprisingly, the generative hybrid outperforms its purely discriminative counterpart, despite losing access to rich source-context features. Finally, we show that machine transliterations have a positive impact on machine translation quality, improving human judgments by 0.5 on a 4-point scale.