Supervised Learning

We describe a series of three experiments in which supervised learning techniques were used to acquire three different types of grammars for English news stories. The acquired grammar types were: …...

We consider the problem of assigning level numbers (weights) to hierarchically organized categories during text categorization. These levels control the ability of the categories to attract documents during the catego- …...

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and …...

This paper discusses the supervised learn- ing of morphology using stochastic trans- ducers, trained using the Expectation- Maximization (EM) algorithm. Two ap- proaches are presented: first, using the transducers directly …...

The paper proposes and empirically moti- vates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In par- ticular, we explore the use of …...

We cluster verbs into lexical semantic classes, using a general set of noisy features that cap- ture syntactic and semantic properties of the verbs. The feature set was previously shown …...

In this paper, we present a corpus-based super- vised word sense disambiguation (WSD) sys- tem for Dutch which combines statistical classi- fication (maximum entropy) with linguistic in- formation. Instead of …...

Traditionally, coreference resolution is done by mining the reference relationships be- tween NP pairs. However, an individual NP usually lacks adequate description informa- tion of its referred entity. In this …...

This paper proposes a method to improve the robustness of a word sense disambigua- tion (WSD) system for Japanese. Two WSD classifiers are trained from a word sense-tagged corpus: one …...

A wide range of supervised learning algorithms has been applied to Text Categorization. However, the supervised learning approaches have some problems. One of them is that they require a large, …...

In this paper, we introduce a new semi-supervised learning model for word sense disambiguation based on Kernel Prin- cipal Component Analysis (KPCA), with experiments showing that it can further improve …...

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 …...

Two systems from the University of Minnesota, Duluth participated in various SENSEVAL-3 lexi- cal sample tasks. The supervised learning system is based on lexical features and bagged decision trees. It …...

Ordering information is a difficult but important task for applications generat- ing natural-language text. We present a bottom-up approach to arranging sen- tences extracted for multi-document sum- marization. To capture …...

We present two machine learning ap- proaches to information extraction from semi-structured documents that can be used if no annotated training data are available, but there does exist a database …...

It has been widely observed that different NLP appli- cations require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are …...

An intelligent thesaurus assists a writer with alternative choices of words and or- ders them by their suitability in the writ- ing context. In this paper we focus on methods …...

In this paper, we described the PNNL Word Sense Disambiguation system as ap- plied to the English all-word task in Se- mEval 2007. We use a supervised learning approach, employing …...

This paper describes a supervised, knowledge-intensive approach to the auto- matic identification of semantic relations between nominals in English sentences. The system employs different sets of new and previously used …...

We propose a new framework for supervised ma- chine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: …...

It is difficult to identify sentence impor- tance from a single point of view. In this paper, we propose a learning-based ap- proach to combine various sentence fea- tures. They …...

We address two problems in the field of au- tomatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating the result with …...

We address the problem of large-scale auto- matic detection of online reviews without us- ing any human labels. We propose an efficient method that combines two basic ideas: Build- ing …...

While extensive studies on relation ex- traction have been conducted in the last decade, statistical systems based on su- pervised learning are still limited because they require large amounts of …...

AbstractWe describe a set of tools, resources, and experiments for opinion classification in business-related datasources in two languages. In particular we concentrate on SentiWordNet text interpretation to produce word, sentence, …...

Many researchers are trying to use information extraction (IE) to create large-scale knowl- edge bases from natural language text on the Web. However, the primary approach (su- pervised learning of …...

We explore the near-synonym lexical choice problem using a novel representa- tion of near-synonyms and their contexts in the latent semantic space. In contrast to traditional latent semantic analysis (LSA), …...

Information-extraction (IE) systems seek to distill semantic relations from natural- language text, but most systems use super- vised learning of relation-specific examples and are thus limited by the availability of …...

In order to automatically extract opinion holders, we propose to harness the con- texts of prototypical opinion holders, i.e. common nouns, such as experts or an- alysts, that describe particular …...

We present a novel computational formula- tion of speaker authority in discourse. This notion, which focuses on how speakers posi- tion themselves relative to each other in dis- course, is …...

Automatic evaluation metrics are fundamen- tally important for Machine Translation, al- lowing comparison of systems performance and efficient training. Current evaluation met- rics fall into two classes: heuristic approaches, like …...

Most event extraction systems are trained with supervised learning and rely on a col- lection of annotated documents. Due to the domain-specificity of this task, event extraction systems must be …...

In this paper, we present a supervised learning approach to training submodu- lar scoring functions for extractive multi- document summarization. By taking a structured prediction approach, we pro- vide a …...

Wikipedia is a Web based, freely available multilingual encyclopedia, constructed in a collaborative effort by thousands of contribu- tors. Wikipedia articles on the same topic in different languages are connected …...

To build a coreference resolver for a new language, the typical approach is to first coreference-annotate documents from this tar- get language and then train a resolver on these annotated …...

AbstractSupervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate …...

A Supervised learning for the identification of semantic relations in parallel enumerative structures This work falls within the framework of ontology engineering and learning from encyclopedic or scientific texts. Our …...

AbstractThere have been several efforts to extend distributional semantics beyond individual words, to measure the similarity of word pairs, phrases, and sentences (briefly, tuples; ordered sets of words, contiguous or …...

This paper proposes to study the problem of identifying intention posts in online discus- sion forums. For example, in a discussion fo- rum, a user wrote “I plan to buy …...

This paper studies the problem of identifying users who use multiple userids to post in so- cial media. Since multiple userids may belong to the same author, it is hard …...

Supervised training procedures for seman- tic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they …...

Sentiment analysis refers to automatically ex- tracting the sentiment present in a given natu- ral language text. We present our participation to the SemEval2013 competition, in the senti- ment analysis …...

Prior research into learning translations from source and target language monolingual texts has treated the task as an unsupervised learn- ing problem. Although many techniques take advantage of a seed …...

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, the surface forms of morphemes. Our focus is on a low- resource learning setting, in which only …...

In this paper we introduce our contribution to the SemEval-2013 Task 2 on “Sentiment Analysis in Twitter”. We participated in “task B”, where the objective was to build mod- els …...

Recently there has been growing interest in the application of approaches from the text classi- fication literature to fine-grained problems of textual stylometry. This paper seeks to answer a question …...

Currently most of state-of-the-art methods for Chinese word segmentation (CWS) are based on supervised learning, which depend on large scale annotated corpus. However, these supervised methods do not work well …...

While discussing a concrete controversial topic, most humans will find it challenging to swiftly raise a diverse set of convincing and relevant claims that should set the basis of their …...

Analogies are considered to be one of the core concepts of human cognition and communica- tion, and are very efficient at encoding com- plex information in a natural fashion. How- …...

This work analyses various syntactic and lexical features for sentence level aspect based sentiment analysis. The task fo- cuses on detection of a writer’s sentiment towards an aspect which is …...

Accurate dependency parsing requires large treebanks, which are only available for a few languages. We propose a method that takes advantage of shared structure across languages to build a mature …...

This paper describes Al-Bayan team participa- tion in SemEval-2015 Task 3, Subtask A. Task 3 targets semantic solutions for answer selec- tion in community question answering systems. We propose a …...

Supervised machine learning classifica- tion algorithms assume both train and test data are sampled from the same domain or distribution. However, performance of the algorithms degrade for test data from …...

Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We for- mulate the problem of classifying tweet level …...

Inferring latent attributes of online users has many applications in public health, politics, and marketing. Most existing approaches rely on supervised learning algorithms, which re- quire manual data annotation and …...

Cross-domain sentiment classification (CSC) aims at learning a sentiment classifier for unlabeled data in the target domain based on the labeled data from a different source domain. Due to the …...

Document sentiment classification is of- ten processed by applying machine learn- ing techniques, in particular supervised learning which consists basically of two major steps: feature extraction and train- ing the …...

Engaging in a debate with oneself or others to take decisions is an integral part of our day-to- day life. A debate on a topic (say, use of per- formance …...

In this paper, we develop a supervised learning technique that improves noisy phrase translation scores obtained by phrase table triangulation. In particular, we extract word translation distributions from small amounts …...

Due to the commonality in natural language, negation focus plays a critical role in deep understanding of context. However, existing studies for negation focus identification ma- jor on supervised learning …...

AbstractCyberbullying statistics are shocking, the number of affected young people is increasing dramatically with the affordability of mobile technology devices combined with a growing number of social networks. This paper …...

We study the automatic detection of sug- gestion expressing text among the opin- ionated text. The examples of such sug- gestions in online reviews would be, cus- tomer suggestions about …...

AbstractIn this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact …...

Extraction and interpretation of temporal information from clinical text is essential for clinical practitioners and researchers. SemEval 2016 Task 12 (Clinical TempEval) addressed this challenge using the THYME1 corpus, a …...

Taxonomic relation identification aims to rec- ognize the ‘is-a’ relation between two terms. Previous works on identifying taxonomic re- lations are mostly based on statistical and lin- guistic approaches, but …...

AbstractIn distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are …...

Resorting to community question answer- ing (CQA) websites for finding answers has gained momentum in the past decade with the explosive rate at which social media has been proliferating. With …...

AbstractWe extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90.70% …...

Recent work has shown that simple vector subtraction over word embeddings is surpris- ingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing …...

AbstractIn the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled …...

AbstractModern models of event extraction for tasks like ACE are based on supervised learning of events from small hand-labeled data. However, hand-labeled training data is expensive to produce, in low …...

AbstractTraditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As …...

AbstractThis paper evaluates different techniques for building a supervised, multilanguage keyphrase extraction pipeline for languages which lack a gold standard. Starting from an unsupervised English keyphrase extraction pipeline, we implement …...

AbstractWe show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, …...

AbstractClinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text. We present a system for this task that uses supervised learning for the …...

AbstractWe study the problem of automatically identifying humorous text from a new kind of text data, i.e., online reviews. We propose a generative language model, based on the theory of …...

AbstractIn this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled “Sentiment analysis in Twitter”, specifically subtask 4A-Arabic. We propose two Arabic sentiment …...

AbstractIn supervised learning of morphological patterns, the strategy of generalizing inflectional tables into more abstract paradigms through alignment of the longest common subsequence found in an inflection table has been …...

Large collections of texts are commonly generated by large organizations and making sense of these collections of texts is a significant challenge. One method for handling this is to organize …...

AbstractNamed Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but …...

Emoji are pictographs commonly used in microblogs as emotion markers, but they can also represent a much wider range of concepts. Additionally, they may occur in different positions within a …...

A sampling survey of typology and component ratio analysis in Japanese puns revealed that the type of Japanese pun that had the largest proportion was a pun type with two …...

We explore ways of identifying terms from monolingual texts and integrate them into investigating the contribution of terminology to translation quality.The researchers proposed a supervised learning method using common statistical …...

Negation is an important contextual phenomenon that needs to be addressed in sentiment analysis. Next to common negation function words, such as not or none, there is also a considerably …...

AbstractThe paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask …...

AbstractFundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations …...

AbstractThe state-of-the-art methods for relation classification are primarily based on deep neural net- works. This kind of supervised learning method suffers from not only limited training data, but also the …...

AbstractCybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware …...

AbstractThis paper discusses the experiments performed for predicting the emotion intensity in tweets using a generalized supervised learning approach. We extract 3 kind of features from each of the tweets …...

AbstractOur submission to the SemEval-2018 Task1: Affect in Tweets shared task competition is a supervised learning model relying on standard lexicon features coupled with word embedding features. We used an …...

The correct analysis of the output of a program based on supervised learning is inevitable in order to be able to identify the errors it produced and characterise its error …...

AbstractGenerating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of tokens always suffers from …...

AbstractIn the effort to tackle the challenge of Hyperpartisan News Detection, i.e., the task of deciding whether a news article is biased towards one party, faction, cause, or person, we …...

AbstractMost existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, …...

AbstractMultiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single …...

AbstractRecognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event …...

AbstractWe investigate the political roles of “Internet trolls” in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention …...

AbstractRelation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine …...

AbstractTintin, the system proposed by the CECL for the Hyperpartisan News Detection task of SemEval 2019, is exclusively based on the tokens that make up the documents and a standard …...

AbstractText classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of …...

AbstractAspect-based sentiment analysis involves the recognition of so called opinion target expressions (OTEs). To automatically extract OTEs, supervised learning algorithms are usually employed which are trained on manually annotated corpora. …...

AbstractExisting approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of …...

AbstractAcademia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily …...

AbstractModern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions, …...

AbstractExploiting a common language as an auxiliary for better translation has a long tradition in machine translation and lets supervised learning-based machine translation enjoy the enhancement delivered by the well-used …...

AbstractA fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction. Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where …...

AbstractThe Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English …...

AbstractSince state-of-the-art approaches to offensive language detection rely on supervised learning, it is crucial to quickly adapt them to the continuously evolving scenario of social media. While several approaches have …...

AbstractSupervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement …...

AbstractMulti-document question generation focuses on generating a question that covers the common aspect of multiple documents. Such a model is useful in generating clarifying options. However, a naive model trained …...

AbstractCrowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the …...

Much of NLP research has focused on crowd- sourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries et …...

AbstractNeural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker’s persona. These models are trained with fully supervised learning …...

AbstractWe take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset …...

AbstractPublicly available, large pretrained Language Models (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks …...

AbstractEvent extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex …...

AbstractIn simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in …...

AbstractNamed Entity Recognition (NER) is an important task in information extraction. However, due to the lack of labelled corpora, biomedical NER has scarcely been studied in Vietnamese compared to English. …...

AbstractNovel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of the same …...

AbstractThe quality of artificially generated texts has considerably improved with the advent of transformers. The question of using these models to generate learning data for supervised learning tasks naturally arises, …...

AbstractLabeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications. This …...

AbstractQuality estimation (QE) is the task of predicting quality of outputs produced by machine translation (MT) systems. Currently, the highest-performing QE systems are supervised and require training on data with …...

AbstractTransformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity. A possible remedy is to reduce the sequence length in the intermediate layers …...

AbstractSupervised learning in Neural Machine Translation (NMT) standardly follows a teacher forcing paradigm where the conditioning context in the model’s prediction is constituted by reference tokens, instead of its own …...

AbstractWe propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, …...

AbstractFrozen models trained to mimic static datasets can never improve their performance. Models that can employ internet-retrieval for up-to-date information and obtain feedback from humans during deployment provide the promise …...

AbstractEvent detection (ED) seeks to discover and classify event instances in plain texts. Previous methods for ED typically adopt supervised learning, requiring fully labeled and high-quality training data. However, in …...

AbstractIn supervised learning, a significant amount of data is essential. To achieve this, we generated and evaluated datasets based on a provided dataset using transformer and non-transformer models. By utilizing …...