Метод опорных векторов

This paper presents a method of Japanese dependency structure analysis based on Sup-- port Vector Machines (SVMs). Conventional parsing techniques based on Machine Learn- ing framework, such as Decision Trees …...

We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization perfor- mance even with input data of high dimensional feature spaces. …...

We describe the University of Maryland's su- pervised sense tagger, which participated in the SENSEVAL-2 lexical sample evaluations for En- glish, Spanish, and Swedish; we also present un- official results …...

We explore how active learning with Sup- port Vector Machines works well for a non-trivial task in natural language pro- cessing. We use Japanese word segmenta- tion as a test …...

While the corpus-based research relies on hu- man annotated corpora, it is often said that a non-negligible amount of errors remain even in frequently used corpora such as Penn Treebank. …...

Named Entity (NE) recognition is a task in which proper nouns and numerical information are ex- tracted from documents and are classified into cat- egories such as person, organization, and …...

Extracting sentences that contain important in- formation from a document is a form of text summarization. The technique is the key to the automatic generation of summaries similar to those …...

This paper proposes a learning and extracting method of word sequence correspondences from non-aligned parallel corpora with Support Vector Machines, which have high ability of the generaliza- tion, rarely cause …...

We explore the use of Support Vector Ma- chines (SVMs) for biomedical named en- tity recognition. To make the SVM train- ing with the available largest corpus – the GENIA …...

This paper introduces a novel Support Vec- tor Machines (SVMs) based voting algorithm for reranking, which provides a way to solve the sequential models indirectly. We have presented a risk …...

Kernel-based learning (e.g., Support Vec- tor Machines) has been successfully ap- plied to many hard problems in Natural Language Processing (NLP). In NLP, al- though feature combinations are crucial to …...

We present an approach to named entity recog- nition that uses support vector machines to cap- ture transition probabilities in a lattice. The support vector machines are trained with hun- …...

In this paper we have designed and experi- mented novel convolution kernels for automatic classification of predicate arguments. Their main property is the ability to process struc- tured representations. Support …...

The identification of authorship falls into the category of style classification, an interesting sub-field of text categorization that deals with properties of the form of linguistic expression as opposed to …...

We present a novel discriminative approach to parsing inspired by the large-margin criterion underlying sup- port vector machines. Our formulation uses a factor- ization analogous to the standard dynamic programs …...

We propose one type of TOP (Tangent vector Of the Posterior log-odds) kernel and apply it to text categorization. In a number of categoriza- tion tasks including text categorization, nega- …...

This paper describes our method based on Support Vector Machines for automatically assigning seman- tic roles to constituents of English sentences. This method employs four different feature sets, one of …...

This paper introduces an approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of po- tentially pertinent information, including several fa- vorability measures for …...

In this paper, we propose a machine learning al- gorithm for shallow semantic parsing, extend- ing the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our …...

We participated in the SENSEVAL-3 English lexi- cal sample task and multilinguallexical sample task. We adopted a supervised learning approach with Support Vector Machines, using only the official training data …...

. We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but …...

We approached the problem as learning how to order documents by estimated relevance with respect to a user query. Our support vector machines based classifier learns from the relevance judgments …...

Research on document similarity has shown that complex representations are not more accurate than the simple bag-of- words. Term clustering, e.g. using latent semantic indexing, word co-occurrences or synonym relations …...

Question classification is an important step in factual question answering (QA) and other dialog systems. Several at- tempts have been made to apply statistical machine learning approaches, including Support Vector …...

Entity relation detection is a form of in- formation extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues …...

Reading proficiency is a fundamen- tal component of language competency. However, finding topical texts at an appro- priate reading level for foreign and sec- ond language learners is a challenge …...

. This paper presents a supervised approach for relation extraction. We apply Support Vector Machines to detect and classify the relations in Automatic Content Extraction (ACE) corpus. We use a …...

In this paper, we describe our systems for the CoNLL-2005 shared task. The aim of the task is semantic role labeling using a machine-learning algorithm. We apply the Support Vector …...

We present a set of experiments involving sentence classification, addressing issues of representation and feature selection, and we compare our findings with simi- lar results from work on the more …...

In recent years tree kernels have been pro- posed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complex- ity and (b) a …...

In this paper, we present a framework for multi-lingual dependency parsing. Our bottom-up deterministic parser adopts Nivre’s algorithm (Nivre, 2004) with a preprocessor. Support Vector Machines (SVMs) are utilized to …...

Deidentification of clinical records is a crucial step before these records can be distributed to non-hospital researchers. Most approaches to deidentification rely heavily on dictionaries and heuristic rules; these approaches …...

This study aims at identifying when an event written in text occurs. In particular, we classify a sentence for an event into four time-slots; morning, daytime, eve- ning, and night. …...

. Word segmentation for Vietnamese, like for most Asian languages, is an important task which has a significant impact on higher language processing levels. However, it has received little attention …...

We investigate the appropriateness of us- ing a technique based on support vector machines for identifying thematic struc- ture of text streams. The thematic seg- mentation task is modeled as …...

This paper presents a method for catego- rizing named entities in Wikipedia. In Wikipedia, an anchor text is glossed in a linked HTML text. We formalize named en- tity categorization …...

This paper proposes the use of Lexical- ized Tree-Adjoining Grammar (LTAG) for- malism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set …...

We study the impact of syntactic and shallow semantic information in automatic classifi- cation of questions and answers and answer re-ranking. We define (a) new tree struc- tures based on …...

This paper addresses the automatic classifi- cation of semantic relations in noun phrases based on cross-linguistic evidence from a set of five Romance languages. A set of novel semantic and …...

This document describes the Word Sense Disam- biguation system used by Language Computer Cor- poration at English Coarse Grained All Word Task at SemEval 2007. The system is based on …...

In this paper, we describe a two-stage multi- lingual dependency parser used for the mul- tilingual track of the CoNLL 2007 shared task. The system consists of two compo- nents: …...

. This paper proposes a convolution tree kernel-based approach for relation extraction where the parse tree is expanded with entity features such as entity type, subtype, and mention level etc. …...

We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of …...

We describe a supervised learning approach to categorizing inter-noun relations, based on Support Vector Machines, that builds a differ- ent classifier for each of seven semantic rela- tions. Each model …...

We describe two systems participating of the English Lexical Sample task in SemEval- 2007. The systems make use of Inductive Logic Programming for supervised learning in two different ways: (a) …...

Combination of features contributes to a significant improvement in accuracy on tasks such as part-of-speech (POS) tag- ging and text chunking, compared with us- ing atomic features. However, selecting combination …...

The Named Entity Recognition (NER) task has been garnering significant attention in NLP as it helps improve the performance of many natural language processing applica- tions. In this paper, we …...

Event reference identification is often treated as a sentence level classification task. However, several different event ref- erences can occur within a single sentence. We present a set of experiments …...

AbstractThis paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of other’s opinions on an …...

This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas …...

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two ma- jor drawbacks: first, the implicit representa- tion of feature …...

Experimenting with different mathematical objects for text representation is an important step of building text classification models. In order to be efficient, such objects of a for- mal model, like …...

The design of practical language applica- tions by means of statistical approaches requires annotated data, which is one of the most critical constraint. This is par- ticularly true for Spoken …...

We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernel- based machine, e.g. Support …...

Automatic detection of general relations between short texts is a complex task that cannot be carried out only relying on lan- guage models and bag-of-words. There- fore, learning methods to …...

. The ability to accurately classify temporal relations between events is an important task in a large number of natural language processing and text mining applications such as question answering, …...

Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information …...

In this paper we introduce the new task of social event extraction from text. We distin- guish two broad types of social events depend- ing on whether only one or …...

A central issue for making the content of a scientific document quickly acces- sible to a potential reader is the extrac- tion of keyphrases, which capture the main topic of …...

AbstractResearch on automatic humor recognition has developed several features which discriminate funny text from ordinary text. The features have been demonstrated to work well when classifying the funniness of single …...

We present an approach for semantic re- lation extraction between nominals that combines semantic information with shal- low syntactic processing. We propose to use the ResearchCyc knowledge base as a …...

This paper summarizes our participation in task #17 of SemEval–2 (All–words WSD on a specific domain) using a su- pervised class-based Word Sense Disam- biguation system. Basically, we use Sup- …...

AbstractExisting approaches to classifying documents by sentiment include machine learning with features created from n-grams and part of speech. This paper explores a different approach and examines performance of one …...

AbstractIn recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective and subjective text, i.e. subjectivity detection, and the distinction between …...

s using Lexical Association⋆ Jonathon Read, Jonathan Webster, and Alex Chengyu Fang The Dialogue Systems Group Department of Chinese, Translation and Linguistics City University of Hong Kong Tat Chee Avenue, …...

Approaches based on machine learning, such as Support Vector Machines, are of- ten used to classify semantic relations be- tween entities. In such framework, classi- fication accuracy strongly depends on …...

Sociolinguists have long argued that social context influences language use in all manner of ways, resulting in lects 1. This paper ex- plores a text classification problem we will call …...

. With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, …...

This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well …...

Psychological disorders are frequently under-diagnosed and consequently have an irreversible impact on individuals and society. The stigma associated with such disorders makes face-to-face discussions with family members and clinicians difficult …...

AbstractIn the authorship identification task, examples of short writings of N authors and an anonymous document written by one of these N authors are given. The task is to determine …...

AbstractRomanian has been traditionally seen as bearing three lexical genders: masculine, feminine and neuter, although it has always been known to have only two agreement patterns (for masculine and feminine). …...

This paper describes University of Leipzig’s approach to SemEval-2013 task 2B on Sen- timent Analysis in Twitter: message polar- ity classification. Our system is designed to function as a baseline, …...

It is important that the testimony of chil- dren be admissible in court, especially given allegations of abuse. Unfortunately, children can be misled by interrogators or might offer false information, …...

In this paper we classify the temporal relations between pairs of events on an article-wide ba- sis. This is in contrast to much of the exist- ing literature which focuses …...

Implicit feature detection, also known as im- plicit feature identification, is an essential as- pect of feature-specific opinion mining but previous works have often ignored it. We think, based on …...

. This paper presents a set of experiments aiming to (1) assess the contribution of NLP to the specific issue of the readability of texts for French as a foreign …...

This paper introduces the first pattern-based Persian Temporal Relation Classifier (PTRC) that finds the type of temporal relations between pairs of events in the Persian texts. The proposed system uses …...

AbstractIn this work, we propose an author-specific sentiment aggregation model for polarity prediction of reviews using an ontology. We propose an approach to construct a Phrase Annotated Author Specific Sentiment …...

AbstractNowadays we are facing a growing demand for semantic knowledge in computational applications, particularly in Natural Language Processing (NLP). However, there aren’t sufficient human resources to produce that knowledge at …...

The results obtained by the BUAP team at Task 1 of SemEval 2014 are presented in this paper. The run submitted is a supervised ver- sion based on two classification …...

We evaluate the use of Deep Belief Net- works as classifiers in a text categorisa- tion task (assigning category labels to doc- uments) in the biomedical domain. Our preliminary results …...

AbstractIn this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the …...

Several state-of-the-art event extraction sys- tems employ models based on Support Vec- tor Machines (SVMs) in a pipeline architec- ture, which fails to exploit the joint depen- dencies that typically …...

This paper presents a method to predict re- trieval terms from relevant/surrounding words or descriptive texts in Japanese by using deep belief networks (DBN), one of two typical types of …...

Paraphrase generation is widely used for various natural language processing (NLP) applications such as question answering, multi-document summarization, and machine translation. In this study, we identify the problems occurring in …...

We present a simple and yet effective ap- proach that can incorporate rationales elicited from annotators into the training of any off- the-shelf classifier. We show that our sim- ple …...

Sarcasm is generally characterized as a figure of speech that involves the substi- tution of a literal by a figurative mean- ing, which is usually the opposite of the original …...

This paper describes the system developed by the University of Texas Health Science Center at Houston (UTHealth), for the 2015 SemEval shared task on “Analysis of Clinical Text” (Task 14). …...

This paper describes our submissions to the Aspect Based Sentiment Analysis task of SemEval-2016. For Aspect Category Detec- tion (Subtask1/Slot1), we used multiple en- sembles, based on Support Vector Machine …...

AbstractIn this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: …...

In this paper, we demonstrate our approach for identification of events, time expressions and temporal relations among them. This work was carried out as part of SemEval-2016 Challenge Task 12: …...

AbstractMonitoring social media has been shown to be an interesting approach for the early detection of drug adverse effects. In this paper, we describe a system which extracts medical entities …...

AbstractSemantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features …...

We describe the system submitted to the SemEval-2016 for detecting stance in tweets (Task 6, Subtask A). One of the main goals of stance detection is to automatically determine the …...

AbstractThis paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification. Algorithms were trained to …...

AbstractMusic information retrieval has emerged as a mainstream research area in the past two decades. Experiments on music mood classification have been performed mainly on Western music based on audio, …...

AbstractThere have been many attempts at automatically recognising author personality traits from text, typically incorporating linguistic features with conventional machine learning models, e.g. linear regression or Support Vector Machines. In …...

AbstractIn this paper, we describe the system of the KULeuven-LIIR submission for Clinical TempEval 2017. We participated in all six subtasks, using a combination of Support Vector Machines (SVM) for …...

AbstractRecent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector …...

AbstractMost current approaches in phylogenetic linguistics require as input multilingual word lists partitioned into sets of etymologically related words (cognates). Cognate identification is so far done manually by experts, which …...

This work presents a comparative study between two different approaches to build an automatic classification system for Modality values in the Portuguese language. One approach uses a single multi-class classifier …...

We present the crowdsourcing platform Donnez Votre Français à la Science (DFS, or “Give your French to Science”), which aims to collect linguistic data and document language use, with a …...

The ability to model and automatically detect dialogue act is an important step toward understanding spontaneous speech and Instant Messages. However, it has been difficult to infer a dialogue act …...

AbstractShort-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space. In this study, we aim to analyze and classify Turkish tweets based on …...

AbstractThe International Classification of Diseases (ICD) is a system for systematically recording patients’ diagnoses. Clinicians or professional coders assign ICD codes to patients’ medical records to facilitate funding, research, and …...

AbstractFeature engineering is an important step in classical NLP pipelines, but machine learning engineers may not be aware of the signals to look for when processing foreign language text. The …...

AbstractIn low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging problem, as even …...

AbstractThe rapid growth of online communication using social media platforms has led to an increase in the presence of hate speech, especially in terms of sexist language online. The proliferation …...

AbstractStance prediction - the computational task of inferring attitudes towards a given target topic of interest - relies heavily on text data provided by social media or similar sources, but …...

AbstractThis paper presents a study on the effectiveness of various approaches for addressing the challenge of multilingual sentiment analysis in low-resource African languages. . The approaches evaluated in the study …...