Expectation Maximization

We describe two new strategies to automatic bracketing of parallel corpora, with particular appli- cation to languages where prior grammar resources are scarce: (1) coarse bilingual grammars, and (2) unsupervised …...

Many recent statistical parsers rely on a preprocess- ing step which uses hand-written, corpus-specific rules to augment the training data with extra infor- mation. For example, head-finding rules are used …...

Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appealing alternative to the Expectation- Maximization …...

This paper discusses the application of the Expectation-Maximization (EM) clustering algorithm to the task of Chinese verb sense discrimination. The model utilized rich linguistic features that capture predicate- argument structure …...

This paper presents Domain Relevance Estima- tion (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain …...

We propose an unsupervised Expectation Maximization approach to pronoun reso- lution. The system learns from a fixed list of potential antecedents for each pro- noun. We show that unsupervised learn- …...

Unsupervised learning algorithms based on Expectation Maximization (EM) are often straightforward to implement and provably converge on a local likelihood maximum. However, these algorithms of- ten do not perform well …...

Dialog act (DA) tags are useful for many applications in natural language process- ing and automatic speech recognition. In this work, we introduce hidden backoff models (HBMs) where a large …...

When a word sense disambiguation (WSD) system is trained on one domain but ap- plied to a different domain, a drop in ac- curacy is frequently observed. This high- lights …...

This paper investigates why the HMMs es- timated by Expectation-Maximization (EM) produce such poor results as Part-of-Speech (POS) taggers. We find that the HMMs es- timated by EM generally assign …...

We propose using large-scale clustering of de- pendency relations between verbs and multi- word nouns (MNs) to construct a gazetteer for named entity recognition (NER). Since depen- dency relations capture …...

We describe a graph-based approach to Sce- nario Template Creation, which is the task of creating a representation of multiple re- lated events, such as reports of different hur- ricane …...

This paper describes a lexical trigger model for statistical machine translation. We present various methods using triplets incorporating long-distance dependencies that can go be- yond the local context of phrases …...

We present an algorithm for pronoun- anaphora (in English) that uses Expecta- tion Maximization (EM) to learn virtually all of its parameters in an unsupervised fashion. While EM frequently fails …...

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 …...

The ability to accurately determine temporal relations between events is an important task for several natural language processing applications such as Question Answering, Summarization, and Information Extraction. Since current supervised …...

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 …...

The Constituent-Context Model (CCM) achieves promising results for unsupervised grammar induction. However, its performance drops for longer sentences. In this paper, we describe a general feature-based model for CCM, in …...

We introduce a novel approach named unam- biguity regularization for unsupervised learn- ing of probabilistic natural language gram- mars. The approach is based on the observa- tion that natural language …...

We present a general framework containing a graded spectrum of Expectation Maximization (EM) algorithms called Unified Expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing al- …...

Domain adaptation has been popularly stud- ied on exploiting labeled information from a source domain to learn a prediction model in a target domain. In this paper, we develop a …...

The popularity of the user generated content, such as Twitter, has made it a rich source for the sentiment analysis and opinion min- ing tasks. This paper presents our study …...

Expectation-maximization algorithms, such as those implemented in GIZA++ pervade the field of unsupervised word alignment. However, these algorithms have a problem of over-fitting, leading to “garbage collector effects,” where rare …...

IBM Model 1 is a classical alignment model. Of the first generation word-based SMT models, it was the only such model with a concave objective function. For concave optimization problems …...

We introduce a latent activity model for work- place emails, positing that communication at work is purposeful and organized by activities. We pose the problem as probabilistic infer- ence in …...

Word embedding has been widely studied and proven helpful in solving many natural lan- guage processing tasks. However, the ambi- guity of natural language is always a prob- lem on …...

We describe a search algorithm for opti- mizing the number of latent states when estimating latent-variable PCFGs with spectral methods. Our results show that contrary to the common belief that …...

AbstractUnsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or learning discriminative …...

AbstractIn this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. …...

AbstractFinding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work …...

AbstractData-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently …...

AbstractWeakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in a corresponding image using only caption-image annotations, i.e., without phrase-object annotations. Previous methods …...

AbstractCrowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations. Though there are a few …...

AbstractDiscovering new intents is of great significance for establishing the Task-Oriented Dialogue System. Most existing methods either cannot transfer prior knowledge contained in known intents or fall into the dilemma …...

AbstractDialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great …...

AbstractMost previous work in music emotion recognition assumes a single or a few song-level labels for the whole song. While it is known that different emotions can vary in intensity …...

AbstractWe present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple phonological changes between ancient word forms …...