Модель максимальной энтропии

We propose a statistical dialogue analysis model to determine discourse structures as well as speech acts using maximum entropy model. The model can automatically acquire probabilistic discourse knowledge from a …...

This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Each has innate strengths and …...

Long sentence analysis has been a critical problem because of high complexity. This pa- per addresses the reduction of parsing com- plexity by intra-sentence segmentation, and presents maximum entropy model …...

This paper presents a new approach to syntac- tic disambiguation based on lexicalized gram- mars. While existing disambiguation mod- els decompose the probability of parsing re- sults into that of …...

∗ Traditional word alignment approaches cannot come up with satisfactory results for Named Entities. In this paper, we propose a novel approach using a maximum entropy model for named entity …...

The HKUST word sense disambiguation systems benefit from a new nonlinear Kernel Principal Component Analysis (KPCA) based disambigua- tion technique. We discuss and analyze results from the Senseval-3 English, Chinese, …...

. This paper proposes a chunking strategy to detect unknown words in Chinese word segmentation. First, a raw sentence is pre-segmented into a sequence of word atoms 1 using a …...

. Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions. It concerns a process of automatically labeling the image contents with …...

. In this paper, we explore the syntactic relation patterns for open- domain factoid question answering. We propose a pattern extraction method to extract the various relations between the proper …...

In this paper, we propose an approach for inferring semantic role using sub- categorization frames and maximum entropy model. Our approach aims to use the sub-categorization information of the verb …...

This paper shows that two uncertainty- based active learning methods, combined with a maximum entropy model, work well on learning English verb senses. Data analysis on the learning process, based …...

In this paper, we explore correlation of dependency relation paths to rank candi- date answers in answer extraction. Using the correlation measure, we compare de- pendency relations of a candidate …...

. Question classification is the first step that Question Answering System must dispose, the precision of question classification greatly affect the subsequent processes. In this paper, we present a new …...

We propose a novel reordering model for phrase-based statistical machine transla- tion (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model …...

In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations generated for a given input …...

Many Web news portals have provided clustered news categories for readers to browse many related news articles. However, to the best of our knowledge, they only provide monolingual services. For …...

A Maximum Entropy Model based system for discriminative learning of syntactic and semantic dependencies submitted to the CoNLL-2008 shared task (Surdeanu, et al., 2008) is presented in this paper. The …...

This paper proposes a novel lexicalized ap- proach for rule selection for syntax-based statistical machine translation (SMT). We build maximum entropy (MaxEnt) mod- els which combine rich context informa- tion …...

Morphological disambiguation proceeds in 2 stages: (1) an analyzer provides all possible analyses for a given token and (2) a stochastic disambiguation module picks the most likely analysis in context. …...

. We introduce a method for learning to assign word senses to translation pairs. In our approach, this sense assignment or disambiguation problem is transformed into one on how to …...

Recent work has shown that translating seg- mentation lattices (lattices that encode alterna- tive ways of breaking the input to an MT sys- tem into words), rather than text in …...

This paper presents a direct word reordering model with novel syntax-based features for sta- tistical machine translation. Reordering models address the problem of reordering source lan- guage into the word …...

We employ Maximum Entropy model to con- duct sub-tree alignment between bilingual phrasal structure trees. Various lexical and structural knowledge is explored to measure the syntactic similarity across Chinese-English bi- …...

. The hierarchical phrase-based (HPB) model on the basis of a synchronous context-free grammar (SCFG) is prominent in solving global reorderings. However, the HPB model is inadequate to supervise the …...

In this paper, we introduce a hybrid method to associate English collocations with sense class members chosen from WordNet. Our combinational approach includes a learning-based method, a paraphrase-based method and …...

This paper describes our submission for Se- mEval2013 Task 2: Sentiment Analysis in Twitter. For the limited data condition we use a lexicon-based model. The model uses an af- fective …...

. We propose two discriminative strategies to integrate compound word recognition in a parsing context: (i) compound pregrouping with Conditional Random Fields before parsing, (ii) reranking parses with a maximum …...

. According to Frame Semantics (Fillmore 1976), words’ meaning are best understood considering the semantic frame they play a role in, for the frame is what gives them context. FrameNet …...