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In this paper we describe an unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus. We compare this algorithm to the …...

This I)at)cr d(~scrihcs a, SI);ulish l'arl.-ol: Speech (I'()S) l~a.gger which al,i)lies aad extends Ih'ill's a, lguril;hn~ for 'u,.super- vised learuing of ruh',- based l, aggcrs (llrill, 1995). First, we discuss …...

This paper presents a corpus-based method to assign grammatical subject/object re- lations to ambiguous German constructs. It makes use of an unsupervised learning procedure to collect training and test data, …...

In this paper we describe a method for per- forming word sense disambiguation (WSD). The method relies on unsupervised learning and ex- ploits functional relations among words as pro- duced …...

This paper describes unsupervised learn- ing algorithm for disambiguating verbal word senses using term weight learning. In our method, collocations which char- acterise every sense are extracted using similarity-based estimation. …...

This paper proposes a new unsupervised learning method for obtaining English part-of- speech(POS) disambiguation rules which would improve the accuracy of a POS tagger. This method has been implemented in …...

In this paper are described experiments on un- supervised learning of the domain lexicon and relevant phrase fragments from a dialog cor- pus. Suggested approach is based on using do- …...

While paraphrasing is critical both for interpretation and generation of natu- ral language, current systems use man- ual or semi-automatic methods to col- lect paraphrases. We present an un- supervised …...

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 …...

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not rec- ommended (thumbs down). The classifi- cation of a review is predicted by …...

Automatic word segmentation is a basic re- quirement for unsupervised learning in morpho- logical analysis. In this paper, we formulate a novel recursive method for minimum descrip- tion length (MDL) …...

We have participated in both English all words 3.task and English lexical sample task of SENSEVAL- Our system disambiguates senses of a target word in a context by selecting a …...

A given entity, representing a person, a location or an organization, may be mentioned in text in multiple, ambiguous ways. Understanding natural language requires identifying whether different mentions of a …...

Paraphrase recognition is a critical step for nat- ural language interpretation. Accordingly, many NLP applications would benefit from high coverage knowledge bases of paraphrases. However, the scal- ability of state-of-the-art …...

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 …...

We present a practically unsupervised learning method to produce single-snippet answers to definition questions in ques- tion answering systems that supplement Web search engines. The method exploits on-line encyclopedias and …...

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 …...

AbstractThis work adresses the use of confidence measures for extracting well recognized words with very low error rate from automatically transcribed segments in a unsupervised way. We present and compare …...

We present a novel approach for discov- ering word categories, sets of words shar- ing a significant aspect of their mean- ing. We utilize meta-patterns of high- frequency words and …...

This paper presents an adaptive learning framework for Phonetic Similarity Modeling (PSM) that supports the automatic construction of transliteration lexicons. The learning algorithm starts with minimum prior knowledge about machine …...

This paper demonstrates how unsupervised tech- niques can be used to learn models of deep linguis- tic structure. Determining the semantic roles of a verb’s dependents is an important step …...

Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and max- imize the probability of the hidden struc- ture given the …...

In unsupervised learning, where no train- ing takes place, one simply hopes that the unsupervised learner will work well on any unlabeled test collection. How- ever, when the variability in …...

We describe an approach for acquiring the domain-specific dialog knowledge required to configure a task-oriented dialog system that uses human-human interaction data. The key aspects of this problem are the …...

. In this paper we present a simple approach to discover gender and animacy knowledge for person mention detection. We learn noun-gender and noun-animacy pair counts from web-scale n-grams using …...

We employ statistical methods to analyze, generate, and translate rhythmic poetry. We first apply unsupervised learning to reveal word-stress patterns in a corpus of raw poetry. We then use these …...

We describe a novel approach to unsuper- vised learning of the events that make up a script, along with constraints on their temporal ordering. We collect natural- language descriptions of …...

Current approaches for semantic parsing take a supervised approach requiring a consider- able amount of training data which is expen- sive and difficult to obtain. This supervision bottleneck is one …...

. Models of word meaning, built from a corpus of text, have demonstrated success in emulating human performance on a number of cognitive tasks. Many of these models use geometric …...

In this paper, we consider the problem of un- supervised morphological analysis from a new angle. Past work has endeavored to design un- supervised learning methods which explicitly or implicitly …...

In this paper we introduce an unsupervised learning approach for WordNet construction. The whole construction method is an Expecta- tion Maximization (EM) approach which uses Princeton WordNet 3.0 (PWN) and …...

In previous work on unsupervised learning of morphology, the long-tail pattern in the rank-frequency distribution of words, as well as of morphological units, is usually considered as following Zipf’s law …...

Studies of computational models of language acquisition depend to a large part on the input available for experiments. In this paper, we study the effect that input size has on …...

We present a global log-linear model for synchronous grammar induction, which is capable of incorporating arbitrary features. The parameters in the model are trained in an unsuper- vised fashion from …...

We present a method that learns repre- sentations for word meanings from short video clips paired with sentences. Un- like prior work on learning language from symbolic input, our input …...

We introduce a social media text normal- ization system that can be deployed as a preprocessing step for Machine Transla- tion and various NLP applications to han- dle social media …...

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 …...

Despite the overwhelming use of statistical language models in speech recognition, machine translation, and several other domains, few high probability guarantees exist on their generaliza- tion error. In this paper, …...

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 …...

AbstractLexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each …...

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 …...

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 …...

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 …...

AbstractThis work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a …...

AbstractIt has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic. This capability …...

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 …...

AbstractObtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what …...

AbstractGenerating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind the majority of natural language processing …...

AbstractKnowledge graphs (KGs) can vary greatly from one domain to another. Therefore supervised approaches to both graph-to-text generation and text-to-graph knowledge extraction (semantic parsing) will always suffer from a shortage …...

AbstractMost existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate this issue with, for instance, …...

AbstractSexism, a form of oppression based on one’s sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, …...

AbstractThis paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent progress in BERT-based language …...

AbstractSemantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous …...

AbstractWe present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. We leverage unsupervised learning in combination with heuristics, taking the best of both …...

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 …...

AbstractDeployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. Prior studies …...

AbstractIn this work, we study the importance of content frequency on abstractive summarization, where we define the content as “semantic units.” We propose a two-stage training framework to let the …...

AbstractDespite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. This challenge is magnified in natural language processing, where no general rules exist for data …...