LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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LIBSVM: A library for support vector machines

National Institute of Standards and Technology における超伝導研究及び生活

This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark 1 . The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.

The PASCAL Recognising Textual Entailment Challenge

Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations.

This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. The paper also introduces TEXTRUNNER, a fully implemented, highly scalable OIE system where the tuples are assigned a probability and indexed to support efficient extraction and exploration via user queries.

We report on experiments over a 9,000,000 Web page corpus that compare TEXTRUNNER with KNOWITALL, a state-of-the-art Web IE system. TEXTRUNNER achieves an error reduction of 33% on a comparable set of extractions. Furthermore, in the amount of time it takes KNOWITALL to perform extraction for a handful of pre-specified relations, TEXTRUNNER extracts a far broader set of facts reflecting orders of magnitude more relations, discovered on the fly. We report statistics on TEXTRUNNER's 11,000,000 highest probability tuples, and show that they contain over 1,000,000 concrete facts and over 6,500,000 more abstract assertions.

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Open information extraction from the web

This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.

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Parallel Data, Tools and Interfaces in OPUS

Each year the Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their systems on exactly the same data sets, in order to better compare systems. The tenth CoNLL (CoNLL-X) saw a shared task on Multilingual Dependency Parsing. In this paper, we describe how treebanks for 13 languages were converted into the same dependency format and how parsing performance was measured. We also give an overview of the parsing approaches that participants took and the results that they achieved. Finally, we try to draw general conclusions about multi-lingual parsing: What makes a particular language, treebank or annotation scheme easier or harder to parse and which phenomena are challenging for any dependency parser?

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CoNLL-X Shared Task on Multilingual Dependency Parsing

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1351324906004505

MaltParser: A language-independent system for data-driven dependency parsing

Sentence fusion is a text-to-text (revision-like) generation task which takes related sentences as input and merges these into a single output sentence In this paper we describe our ongoing work on developing a sentence fusion module for Dutch We propose a generalized version of alignment which not only indicates which words and phrases should be aligned but also labels these in terms of a small set of primitive semantic relations, indicating how words and phrases from the two input sentences relate to each other It is shown that human labelers can perform this task with a high agreement (Fscore of 95) We then describe and evaluate our adaptation of an existing automatic alignment algorithm, and use the resulting alignments, plus the semantic labels, in a generalized fusion and generation algorithm A small-scale evaluation study reveals that most of the resulting sentences are adequate to good

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Explorations in Sentence Fusion

Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a - possibly unfinished - care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task.

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Care episode retrieval: distributional semantic models for information retrieval in the clinical domain.

This paper addresses the classification of semantic relations between pairs of sentences extracted from a Dutch parallel corpus at the word, phrase and sentence level. We first investigate the performance of human annotators on the task of manually aligning dependency analyses of the respective sentences and of assigning one of five semantic relations to the aligned phrases (equals, generalizes, specifies, restates and intersects). Results indicate that humans can perform this task well, with an F-score of .98 on alignment and an F-score of .95 on semantic relations (after correction). We then describe and evaluate a combined alignment and classification algorithm, which achieves an F-score on alignment of .85 (using EuroWordNet) and an F-score of .80 on semantic relation classification.

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Erwin Marsi

Papers

CoNLL-X Shared Task on Multilingual Dependency Parsing

MaltParser: A language-independent system for data-driven dependency parsing

Explorations in Sentence Fusion

Care episode retrieval: distributional semantic models for information retrieval in the clinical domain.

Classification of Semantic Relations by Humans and Machines