Максималды энтропия

We describe work which uses decision trees to estimate marginal probabilities in a maximum entropy model for pre- dicting the part-of-speech of a word given the context in which it …...

This paper presents a statistical model which trains from a corpus annotated with Part-Of- Speech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be …...

This paper presents a statistical parser for natural language that obtains a parsing accuracy--roughly 87% precision and 86% recall--which surpasses the best previously published results on the Wall St. Journal …...

This paper describes a dependency structure analysis of Japanese sentences based on the maximum entropy mod- els. Our model is created by learning the weights of some features from a …...

I describe two methods for incorporating infor- mation about the relative positions of bilingual word pairs into a Maximum Entropy/Minimum Divergence translation model. The better of the two achieves over …...

Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well …...

The Stanford-CS224N system is an ensemble of sim- ple classifiers. The first-tier systems are heteroge- neous, consisting primarily of naive-Bayes variants, but also including vector space, memory-based, and other classifier …...

Named entity (NE) recognition is a task in which proper nouns and nu- merical information in a document are detected and classified into categories such as person, organization, location, and …...

The WSD system presented at SENSEVAL-2 uses a knowledge-based method for noun dis- ambiguation and a corpus-based method for verbs and adjectives. The methods are, respec- tively, Specification Marks and …...

In this paper, we propose a method for learning a classifier which combines out- puts of more than one Japanese named entity extractors. The proposed combi- nation method belongs to …...

This paper presents a maximum entropy-based named entity recognizer (NER). It differs from pre- vious machine learning-based NERs in that it uses information from the whole document to classify each …...

This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the applica- tion being the recovery of named-entity boundaries in a corpus of web data. The …...

Text normalization is an important aspect of successful information retrieval from medical documents such as clinical notes, radiology reports and discharge summaries. In the medical domain, a significant part of …...

A maximum entropy (ME) model is usu- ally estimated so that it conforms to equal- ity constraints on feature expectations. However, the equality constraint is inap- propriate for sparse and …...

The development of FrameNet, a large database of semantically annotated sen- tences, has primed research into statistical methods for semantic tagging. We ad- vance previous work by adopting a Maximum …...

This paper presents a maximum entropy based Chinese named entity recognizer (NER): Mencius. It aims to address Chinese NER problems by combining the advantages of rule-based and machine learning (ML) …...

In our information era, keywords are very useful to information retrieval, text clustering and so on. News is always a domain attracting a large amount of attention. Aiming at news …...

Conditional random fields for sequence label- ing offer advantages over both generative mod- els like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, …...

We present an application of ambiguity pack- ing and stochastic disambiguation techniques for Lexical-Functional Grammars (LFG) to the domain of sentence condensation. Our system incorporates a linguistic parser/generator for LFG, …...

In this paper, we present a corpus-based super- vised word sense disambiguation (WSD) sys- tem for Dutch which combines statistical classi- fication (maximum entropy) with linguistic in- formation. Instead of …...

This paper proposes a new approach for coreference resolution which uses the Bell tree to represent the search space and casts the coreference resolution problem as finding the best path …...

A novel technique for maximum “a posteriori” (MAP) adaptation of maximum entropy (MaxEnt) and maximum entropy Markov models (MEMM) is presented. The technique is applied to the problem of recov- …...

Traditionally, word sense disambiguation (WSD) involves a different context model for each individual word. This paper presents a new approach to WSD using weakly supervised learning. Statistical models are not …...

We perform Noun Phrase Bracketing by using a lo- cal, maximum entropy-based tagging model, which produces bracketing hypotheses. These hypothe- ses are subsequently fed into a reranking frame- work based …...

In this paper, we propose a semantic role label- ing method using a maximum entropy model, which enables not only to exploit rich features but also to alleviate the data …...

We describe a statistical approach to semantic role labelling that employs only shallow infor- mation. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between …...

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 this paper, we describe our experiments on statistical word sense disambiguation (WSD) using two systems based on different ap- proaches: Na¨ıve Bayes on word tokens and Max- imum Entropy …...

It is fairly common that different people are associated with the same name. In tracking person entities in a large document pool, it is important to determine whether multiple mentions …...

Recently, shallow parsing has been applied to various information processing systems, such as information retrieval, information extraction, question answering, and automatic document summarization. A shallow parser is suitable for online …...

This paper investigates automatic identi- fication of Information Structure (IS) in texts. The experiments use the Prague Dependency Treebank which is annotated with IS following the Praguian approach of Topic …...

In this paper, we propose a method that exploits full parsing information by repre- senting it as features of argument classifi- cation models and as constraints in integer linear learning …...

Processing unknown words is disproportionately important because of their high information con- tent. It is crucial in domains with specialist vocab- ularies where relevant training material is scarce, for example: …...

. Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread am- biguity of terms out of context and extensive lexical variations. This …...

Traditionally, word sense disambiguation (WSD) involves a different context classifi- cation model for each individual word. This paper presents a weakly supervised learning approach to WSD based on learning a …...

The conventional n-gram language model exploits only the immediate context of historical words without exploring long-distance semantic information. In this paper, we present a new information source extracted from latent …...

This paper presents a new approach to combining outputs of existing word align- ment systems. Each alignment link is rep- resented with a set of feature functions extracted from linguistic …...

Much work on information extraction has successfully used gazetteers to recognise uncommon entities that cannot be reliably identified from local context alone. Ap- proaches to such tasks often involve the …...

This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) …...

Automatic segmentation is important for making multimedia archives comprehensi- ble, and for developing downstream infor- mation retrieval and extraction modules. In this study, we explore approaches that can segment multiparty …...

In this paper we describe an automatic prosody labeling framework that exploits both language and speech information. We model the syntactic-prosodic informa- tion with a maximum entropy model that achieves …...

This document describes the Word Sense Disam- biguation system used by Language Computer Cor- poration at English Coarse Grained All Word Task at SemEval 2007. The system is based on …...

In this paper, we described the PNNL Word Sense Disambiguation system as ap- plied to the English all-word task in Se- mEval 2007. We use a supervised learning approach, employing …...

. This paper reports about a multi-engine approach for the development of a NER system in Bengali by combining the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) …...

We show that jointly parsing a bitext can sub- stantially improve parse quality on both sides. In a maximum entropy bitext parsing model, we define a distribution over source trees, …...

Statistical machine learning methods are em- ployed to train a Named Entity Recognizer from annotated data. Methods like Maxi- mum Entropy and Conditional Random Fields make use of features for …...

In this paper, we investigate how an ac- curate question classifier contributes to a question answering system. We first present a Maximum Entropy (ME) based question classifier which makes use …...

. Part of Speech (POS) tagging is the task of labeling each word in a sentence with its appropriate syntactic category called part of speech. POS tagging is a very …...

AbstractA SuperSense Tagger is a tool for the automatic analysis of texts that associates to each noun, verb, adjective and adverb a semantic category within a general taxonomy. The developed …...

In this paper, it is presented an unsuper- vised approach to automatically discover the latent keyphrases contained in scien- tific articles. The proposed technique is constructed on the basis of …...

This paper describes our system participating in task 18 of SemEval-2010, i.e. disambiguating Sentiment- Ambiguous Adjectives (SAAs). To disambiguating SAAs, we compare the machine learning-based and lexicon- based methods in …...

We propose a method for annotating post- to-post discourse structure in online user forum data, in the hopes of improving troubleshooting-oriented information ac- cess. We introduce the tasks of: (1) …...

AbstractATLIS (short for “ ATLIS Tags Locations in Strings”) is a tool being developed using a maximum-entropy machine learning model for automatically identifying information relating to spatial and locational information …...

We propose the subtree ranking approach to parse forest reranking which is a general- ization of current perceptron-based reranking methods. For the training of the reranker, we extract competing local …...

This paper describes our system participat- ing in the CoNLL-2012 shared task: Mod- eling Multilingual Unrestricted Coreference in Ontonotes. Maximum entropy models are used for our system as classifiers to …...

This paper reports on the annotation and maximum-entropy modeling of the se- mantics of two German prepositions, mit (‘with’) and auf (‘on’). 500 occurrences of each preposition were sampled from …...

The Discriminative Word Lexicon (DWL) is a maximum-entropy model that pre- dicts the target word probability given the source sentence words. We present two ways to extend a DWL to …...

Information extraction systems automati- cally extract structured information from machine-readable documents, such as newswire, web, and multimedia. Despite significant improvement, the performance is far from perfect. Hence, it is useful …...

There have been many recent investigations into methods to tune SMT systems using large numbers of sparse features. However, there have not been nearly so many examples of helpful sparse …...

. The process of understanding a document uses both visual, logic and discursive information along with the mutual relationships between those levels. Most approaches studying those relationships were conducted in …...

AbstractIn this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional …...

This paper describes our SeemGo sys- tem for the task of Aspect Based Sen- timent Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling …...

Interpreting event mentions in text is cen- tral to many tasks from scientific research to intelligence gathering. We present an event trigger detection system and explore baseline configurations. Specifically, we …...

AbstractWe present Sparse Non-negative Matrix (SNM) estimation, a novel probability estimation technique for language modeling that can efficiently incorporate arbitrary features. We evaluate SNM language models on two corpora: the …...

AbstractWe describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) …...

AbstractThis paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task …...

We present the crowdsourcing platform Donnez Votre Français à la Science (DFS, or “Give your French to Science”), which aims to collect linguistic data and document language use, with a …...