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

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

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

https://hal.archives-ouvertes.fr/hal-01838537/document

SemEval-2016 task 5 : aspect based sentiment analysis

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

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SemEval-2017 Task 4: Sentiment Analysis in Twitter

This paper presents a comparison of support vector machines (SVM), memory-based learning (MBL) and Naive Bayes (NB) techniques for the classification of legitimate and spam mails. Although there are a number of method-comparative studies regarding spam mail filtering, most of the studies are tested on separate data sets. In order to evaluate the effectiveness of SVM, MBL and NB methods, we have used a common publicly available corpus (LINGSPAM). As MBL and NB methods are previously tested with this corpus, the obtained best parameters are used in the experiments with few changes. On the other hand, intense experiments are made to find the best attribute dimensions with SVMs. Results show that SVM has significantly better performance for no-cost and high-cost cases, but NB performs best when the cost is extremely high.

/pdf/a-comparison-of-support-vector-machines-memory-based-and-jg59hj2kgn.pdf

A Comparison of Support Vector Machines, Memory-based and Naïve Bayes Techniques on Spam Recognition.


 Abstract meaning representation (AMR) is a graph-based sentence-level meaning representation that has become highly popular in recent years. AMR is a knowledge-based meaning representation heavily relying on frame semantics for linking predicate frames and entity knowledge bases such as DBpedia for linking named entity concepts. Although it is originally designed for English, its adaptation to non-English languages is possible by defining language-specific divergences and representations. This article introduces the first AMR representation framework for Turkish, which poses diverse challenges for AMR due to its typological differences compared to English; agglutinative, free constituent order, morphologically highly rich resulting in fewer word surface forms in sentences. The introduced solutions to these peculiarities are expected to guide the studies for other similar languages and speed up the construction of a cross-lingual universal AMR framework. Besides this main contribution, the article also presents the construction of the first AMR corpus of 700 sentences, the first AMR parser (i.e., a tree-to-graph rule-based AMR parser) used for semi-automatic annotation, and the evaluation of the introduced resources for Turkish.

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Abstract meaning representation of Turkish

This paper presents our preliminary conclusions as part of an ongoing effort to construct a new dependency representation framework for Turkish. We aim for this new framework to accommodate the highly agglutinative morphology of Turkish as well as to allow the annotation of unedited web data, and shape our decisions around these considerations. In this paper, we firstly describe a novel syntactic representation for morphosyntactic sub-word units (namely inflectional groups (IGs) in Turkish) which allows inter-IG relations to be discerned with perfect accuracy without having to hide lexical information. Secondly, we investigate alternative annotation schemes for coordination structures and present a better scheme (nearly 11% increase in recall scores) than the one in Turkish Treebank (Oflazer et al., 2003) for both parsing accuracies and compatibility for colloquial language.

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Representation of Morphosyntactic Units and Coordination Structures in the Turkish Dependency Treebank

Bu makalede, Turkce cumlelerin kural tabanli baglilik analizi yontemi ile ayristirilmalari sonucunda elde edilen basarim sunulmaktadir. Calisma, test verisi olarak kullanilan ODTU-Sabanci Agac Yapili Derlemi'nin butunu uzerindeki ilk kural tabanli sonuclari icermektedir. Uygulanan ayristirma algoritmasi ve kural yapilari detayli olarak verilmistir. Sonuclar Turkce'nin Baglilik Analizi konusunda yapilacak calismalara temel olma niteligindedir.

Türkçe cümlelerin kural tabanlı bağlılık analizi

This paper presents the state-of-the-art results in Turkish coreference resolution (CR) which is a task of determining sets of mentions which identify the same real-world entity (eg a person, a place, a thing, an event) The proposed system uses support vector machines and solves the CR task with a mention-pair model that basically accepts mention couples and decides on whether they are coreferential with each other or not The results are evaluated on Marmara Turkish Coreference Corpus by using well-known evaluation metrics (viz MUC, B3, BLANC and LEA) The introduced approach obtains F1 scores of 9068% (MUC), 8689% (B3), 8513% (BLANC) and 7834% (LEA) yielding an improvement of 912, 1606, 1308 and 1257 percentage points respectively over a recent baseline system on Turkish CR The paper introduces the system setup (SVM parameters and negative sampling strategy) as well as the selected features and analyzes the impact of these features on the Turkish CR task

Gülşen Eryiğit

Papers

A Comparison of Support Vector Machines, Memory-based and Naïve Bayes Techniques on Spam Recognition.

Abstract meaning representation of Turkish

Representation of Morphosyntactic Units and Coordination Structures in the Turkish Dependency Treebank

Türkçe cümlelerin kural tabanlı bağlılık analizi

Turkish Coreference Resolution