Discriminative Model

In this paper we show that generative models are competitive with and some- times superior to discriminative models, when both kinds of models are allowed to learn structures that are …...

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 …...

Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic representation. In the last decade two main approaches have been pursued: native models.generative and discrimi- …...

The character-based tagging approach is a dominant technique for Chinese word segmentation, and both discrimi- native and generative models can be adopted in that framework. However, generative and discriminative charac- …...

AbstractIn the POS tagging task, there are two kinds of statistical models: one is generative model, such as the HMM, the others are discriminative models, such as the Maximum Entropy …...

Features for Robust Cross-Domain Chinese Word Segmentation LI Xiaoqing1, WANG Kun1, ZONG Chengqing1 and SU Keh-Yih2 (1) National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, …...

Microblogging networks serve as vehicles for reaching and influencing users. Predicting whether a message will elicit a user response opens the possibility of maximizing the viral- ity, reach and effectiveness …...

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 introduce a novel discriminative model for phrase-based monolingual alignment using a semi-Markov CRF. Our model achieves state- of-the-art alignment accuracy on two phrase- based alignment datasets (RTE and para- …...

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 …...

Translation into morphologically rich lan- guages is an important but recalcitrant prob- lem in MT. We present a simple and effec- tive approach that deals with the problem in two …...

. We provide a new weighted parsing algorithm for deterministic context free grammar parsing inspired by LR (Knuth, 1965). The parser is weighted by a discriminative model that allows determinism …...

AbstractSentiment analysis is genre and domain dependent, i.e. the same method performs differently when applied to text that originates from different genres and domains. Intuitively, this is due to different …...

We present a discriminative model for de- tecting disfluencies in spoken language tran- scripts. Structurally, our model is a semi- Markov conditional random field with features targeting characteristics unique to …...

We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints. Our model selects textual units to include in the summary based on a rich set …...

AbstractWe present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, …...

AbstractWe describe a weakly-supervised method for training deep learning models for the task of ad-hoc document retrieval. Our method is based on generative and discriminative models that are trained using …...

AbstractAspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework. However, when facing multiple aspect …...