Semi-Supervised Learning

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 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 …...

In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a num- ber of semi-supervised methods have been …...

In this paper we propose and evaluate a technique to perform semi-supervised learning for Text Categorization. particular we defined a kernel function,In namely the Domain Kernel, that allowed us to …...

The applicability of many current information ex- traction techniques is severely limited by the need for supervised training data. We demonstrate that for certain field structured extraction tasks, such as …...

This paper presents a new application of the recently proposed machine learning method Alternating Structure Optimiza- tion (ASO), to word sense disambiguation (WSD). Given a set of WSD problems and …...

We present two semi-supervised learning techniques to improve a state-of-the-art multi-lingual name tagger. For English and Chinese, the overall system obtains 1.7% - 2.1% improvement in F-measure, representing a 13.5% …...

In this paper, we present a method for guessing POS tags of unknown words us- ing local and global information. Al- though many existing methods use only local information (i.e. …...

This paper presents a novel approach to the semi-supervised learning of Informa- tion Extraction patterns. The method makes use of more complex patterns than previous approaches and determines their similarity …...

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. …...

Recognition of tone and intonation is es- sential for speech recognition and lan- guage understanding. However, most ap- proaches to this recognition task have re- lied upon extensive collections of …...

Over the last few years, two of the main research directions in machine learning of natural language processing have been the study of semi-supervised learning algo- rithms as a way …...

In this paper we explore robustness and domain adaptation issues for Word Sense Disambiguation (WSD) using Singular Value Decomposition (SVD) and unlabeled data. We focus on the semi-supervised do- main …...

This paper proposes a semi-supervised learn- ing method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate …...

This paper provides evidence that the use of more unlabeled data in semi-supervised learn- ing can improve the performance of Natu- ral Language Processing (NLP) tasks, such as part-of-speech tagging, …...

This paper presents a semi-supervised model for generating a table-of-contents as an in- dicative summarization. We mainly focus on using word cluster-based information derived from a large amount of unannotated …...

Unknown lexical items present a major obstacle to the development of broad- coverage semantic role labeling systems. We address this problem with a semi- supervised learning approach which ac- quires …...

This paper presents a new approach to selecting the initial seed set using stratified sampling strategy in bootstrapping-based semi-supervised learning for semantic relation classification. First, the training data is partitioned …...

This paper presents a semi-supervised learn- ing framework for mining Chinese-English lexicons from large amount of Chinese Web pages. The issue is motivated by the ob- servation that many Chinese …...

Large scale annotated corpora are pre- requisite to developing high-performance semantic role labeling systems. Unfor- tunately, such corpora are expensive to produce, limited in size, and may not be representative. …...

Semantic Role Labeling (SRL) has proved to be a valuable tool for performing auto- matic analysis of natural language texts. Currently however, most systems rely on a large training set, …...

Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter up- dates can be made. SampleRank is a rank- based learning framework that …...

In this paper, we introduce a method that au- tomatically builds text classifiers in a new lan- guage by training on already labeled data in another language. Our method transfers …...

Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric …...

AbstractIn this work we present SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications. SENTIWORDNET 3.0 is an improved version of SENTIWORDNET 1.0, a …...

We investigate the use of Semi-Supervised Learning (SSL) in opinion detection both in sparse data situations and for domain adapta- tion. We show that co-training reaches the best results in …...

The challenges of Named Entities Recogni- tion (NER) for tweets lie in the insufficient information in a tweet and the unavailabil- ity of training data. We propose to com- bine …...

Extracting biomedical named entities is one of the major challenges in automatic processing of biomedical literature. This paper proposes a machine learning approach for finding phenotype names in text. Features …...

This paper introduces a method to improve supervised word sense disambiguation perfor- mance by including a new class of features which leverage contextual information from large unannotated corpora. This new …...

In this paper, we propose the use of spans in addition to edges in noun compound analysis. A span is a sequence of words that can represent a noun compound. …...

Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking …...

For efficient diagnosis processes, the mul- titude of heterogeneous medical data re- quires seamless integration. In order to automatically align radiology reports and images based on the pathological anatom- ical …...

We present a flexible formulation of semi- supervised learning for structured mod- els, which seamlessly incorporates graph- based and more general supervision by ex- tending the posterior regularization (PR) framework. …...

This paper studies the problem of non- monotonic sentence alignment, motivated by the observation that coupled sentences in real bitexts do not necessarily occur monotonically, and proposes a semisuper- vised …...

Semi-supervised learning (SSL) methods augment standard machine learning (ML) techniques to leverage unlabeled data. SSL techniques are often effective in text classification, where labeled data is scarce but large unlabeled …...

Finding concepts in natural language ut- terances is a challenging task, especially given the scarcity of labeled data for learn- ing semantic ambiguity. Furthermore, data mismatch issues, which arise when …...

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 …...

We experiment graph-based Semi- Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language (SLU) on unaligned data.Understanding The aligned labels for examples are obtained using …...

In this paper, instead of designing new fea- tures based on intuition, linguistic knowl- edge and domain, we learn some new and effective features using the deep auto- encoder (DAE) …...

Graph-based learning algorithms have been shown to be an effective approach for query-focused multi-document summarization (MDS). In this paper, we extend the standard graph ranking algo- rithm by proposing a …...

AbstractSupervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated …...

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach …...

Some languages do not have enough la- beled data to obtain good discourse pars- ing, specially in the relation identification step, and the additional use of unlabeled data is a …...

AbstractIn most of the research studies on Author Profiling, large quantities of correctly labeled data are used to train the models. However, this does not reflect the reality in forensic …...

AbstractRecently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we …...

AbstractThe conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features …...

AbstractIn gender classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for gender classification to improve the performance by exploring the knowledge in both …...

We propose a fast and scalable method for semi-supervised learning of sequence models, based on anchor words and moment matching. Our method can handle hidden Markov mod- els with feature-based …...

AbstractCurrent word alignment models do not distinguish between different types of alignment links. In this paper, we provide a new probabilistic model for word alignment where word alignments are associated …...

AbstractThis paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and …...

AbstractTemporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval …...

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 …...

Image question answering (IQA) is one of the tasks that need rich resources, i.e. supervised data, to achieve optimal performance. However, because IQA is a challenging task that handles complex …...

Parts of Speech (POS) tagging is an important pre-requisite for various Natural Language Processing tasks. POS tagging is rather challenging for morphologically rich languages such as Tamil. Being low-resourced, Tamil …...

AbstractWe combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled …...

Temporal sense detection of any word is an important aspect for detecting temporality at the sentence level. In this paper, at first, we build a temporal resource based on a …...

AbstractWe address the problem of speech act recognition (SAR) in asynchronous conversations (forums, emails). Unlike synchronous conversations (e.g., meetings, phone), asynchronous domains lack large labeled datasets to train an effective …...

AbstractNoise is inherent in real world datasets and modeling noise is critical during training as it is effective in regularization. Recently, novel semi-supervised deep learning techniques have demonstrated tremendous potential …...

AbstractA grand goal in AI is to build a robot that can accurately navigate based on natural language instructions, which requires the agent to perceive the scene, understand and ground …...

AbstractWe study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance. We propose a new annotation schema incorporating domain-specific speech acts, such as …...

AbstractFor machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited …...

AbstractStructured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief trackers often requires expensive turn-level annotations of every user utterance. In …...

AbstractRecent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks. However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples. In …...

AbstractThe CoNLL-2003 corpus for English-language named entity recognition (NER) is one of the most influential corpora for NER model research. A large number of publications, including many landmark works, have …...

AbstractThis paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples …...

AbstractSemi-supervision is a promising paradigm for Bilingual Lexicon Induction (BLI) with limited annotations. However, previous semisupervised methods do not fully utilize the knowledge hidden in annotated and nonannotated data, which …...

AbstractDeep neural networks have made great success on video captioning in supervised learning setting. However, annotating videos with descriptions is very expensive and time-consuming. If the video captioning algorithm can …...

AbstractSemantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic …...

AbstractThis paper describes a novel application of semi-supervision for shallow discourse parsing. We use a neural approach for sequence tagging and focus on the extraction of explicit discourse arguments. First, …...

AbstractThe principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other. While this idea …...

AbstractSemi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of …...

AbstractIn this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task’s main aim was to identify spans to which a given text’s …...

AbstractText in the form of tags associated with online images is often informative for predicting private or sensitive content from images. When using privacy prediction systems running on social networking …...

AbstractSemantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged …...

AbstractMuch of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents. Optical character recognition (OCR) can be used to produce …...

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW). FSR-2020 aims at fostering …...

AbstractUnderstanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of the population as well as provide emotional support to those …...

AbstractDependency parsing is an important fundamental natural language processing task which analyzes the syntactic structure of an input sentence by illustrating the syntactic relations between words. To improve dependency parsing, …...

AbstractA critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time-consuming to obtain. Although a small amount of labeled data …...

AbstractDuring natural disasters, people often use social media platforms, such as Twitter, to post information about casualties and damage produced by disasters. This information can help relief authorities gain situational …...

AbstractSemi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-supervised learning methods achieved impressive performance. In this …...

AbstractThe conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks. However, in …...

AbstractStance detection aims to identify the attitude from an opinion towards a certain target. Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient …...

AbstractThe need for large corpora raw corpora has dramatically increased in recent years with the introduction of transfer learning and semi-supervised learning methods to Natural Language Processing. And while there …...

AbstractSolving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solving MWPs require full supervision in the form of intermediate equations. However, …...

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, …...

AbstractSemi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition …...

AbstractDetecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task …...

AbstractPrompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding (NLU) datasets and tasks in the literature. However, manually designing multiple …...

AbstractWe investigate whether using semi-supervised learning (SSL) methods can be beneficial for the task of word-level Quality Estimation of Machine Translation in low resource conditions. We show that the Mean …...

AbstractDue to the semantic complexity of the Relation extraction (RE) task, obtaining high-quality human labelled data is an expensive and noisy process. To improve the sample efficiency of the models, …...