Максималды энтропия классификаторы

Empirical experience and observations have shown us when powerful and highly tunable classifiers such as maximum en- tropy classifiers, boosting and SVMs are applied to language processing tasks, it is …...

Previous systems that automatically tag text with FrameNet labels have been trained from the FrameNet example data, as there is no FrameNet annotated corpus. The FrameNet data is systemat- ically …...

A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify …...

In this paper we apply conditional random fields (CRFs) to the semantic role labelling task. We define a random field over the structure of each sentence’s syntactic parse tree. For …...

We describe a parser used in the CoNLL 2006 Shared Task, “Multingual Depen- dency Parsing.” The parser first identi- fies syntactic dependencies and then labels those dependencies using a maximum …...

This paper investigates a machine learn- ing approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an over- sampling …...

Automated recognition of the semantic re- lationship between two nouns in a sentence is useful for a wide variety of tasks in NLP. Previous approaches have used ker- nel methods …...

We develop a data set of Malay lexemes la- belled with count classifiers, that are attested in raw or lemmatised corpora. A maximum entropy classifier based on simple, language- inspecific …...

We present a system that finds short def- initions of terms on Web pages. It em- ploys a Maximum Entropy classifier, but it is trained on automatically generated ex- amples; …...

We present a new method that compresses sentences by removing words. In a first stage, it generates candidate compressions by re- moving branches from the source sentence’s dependency tree using …...

BART (Versley et al., 2008) is a highly mod- ular toolkit for coreference resolution that supports state-of-the-art statistical approaches and enables efficient feature engineering. For the SemEval task 1 on …...

Automatic error detection is desired in the post-processing to improve machine translation quality. The previous work is largely based on confidence estimation us- ing system-based features, such as word posterior …...

Maximum Entropy Principle has been used successfully in various NLP tasks. In this paper we propose a forward transla- tion model consisting of a set of maxi- mum entropy classifiers: …...

We present a method that paraphrases a given sentence by first generating candidate para- phrases and then ranking (or classifying) them. The candidates are generated by ap- plying existing paraphrasing …...

This paper describes a novel approach aimed to identify a priori which subset of machine translation (MT) systems among a known set will produce the most reliable translations for a …...

AbstractIn this paper, we investigate the usage of a non-canonical German passive alternation for ditransitive verbs, the recipient passive, in naturally occuring corpus data. We propose a classifier that predicts …...

. This work uses parallel monolingual corpora for a detailed study of the task of sub- sentential paraphrase acquisition. We argue that the scarcity of this type of resource is …...

Linguistic steganography is a form of covert communication using natural language to conceal the existence of the hidden message, which is usually achieved by systematically making changes to a cover …...

Underspecified queries are common in vertical search engines, leading to large result sets that are difficult for users to navigate. In this paper, we show that we can automatically guide …...

We present our entries for the SemEval- 2013 cross-language word-sense disambigua- tion task (Lefever and Hoste, 2013). We submitted three systems based on classifiers trained on local context features, with …...

Lexical chains provide a representation of the lexical cohesion structure of a text. In this pa- per, we propose two lexical chain based co- hesion models to incorporate lexical cohesion …...

We propose a supervised lexical substitu- tion system that does not use separate clas- sifiers per word and is therefore applicable to any word in the vocabulary. Instead of learning …...

Current approaches to document-level sentiment analysis rely on local informa- tion, e.g., the words within the given doc- ument. We try to achieve better perfor- mance by incorporating global context …...

The sense in which a word is used deter- mines the translation of the word. In this paper, we propose a sense-based transla- tion model to integrate word senses into …...

This paper reports our submissions to the four subtasks of Aspect Based Sentimen- t Analysis (ABSA) task (i.e., task 4) in SemEval 2014 including aspect term ex- traction and aspect …...

Phrase reordering is a challenge for statis- tical machine translation systems. Posing phrase movements as a prediction prob- lem using contextual features modeled by maximum entropy-based classifier is su- perior …...

AbstractWe describe a method for expanding existing dictionaries in several languages by discovering previously non-existent links between translations. We call this method triangulation and we present and compare several variations …...

Paraphrase recognition is the task of iden- tifying whether two pieces of natural lan- guage represent similar meanings. This pa- per describes a system participating in the shared task 1 …...

AbstractWe study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In …...

In this paper, we capture the argument rela- tionships for Chinese semantic role labeling task, and improve the task’s performance with the help of argument relationships. We split the relationship …...

In this paper, we present our system devel- oped for the SemEval 2016 Task 11: Com- plex Word Identification. Our team achieved the 3rd place among 21 participants. Our sys- …...

This paper describes our system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. Our system uses Max- imum Entropy classifier for the aspect cate- gory detection …...

AbstractIn this paper, we introduce a cross-lingual Semantic Role Labeling (SRL) system with language independent features based upon Universal Dependencies. We propose two methods to convert SRL annotations from monolingual …...

Discriminating between similar languages (DSL) on conversational texts is a challenging task. This paper aims at discriminating between limited-resource languages on short conversational texts, like Uyghur and Kazakh. Considering that …...