Қазіргі заманғы базалық сызық

We present discriminative reordering models for phrase-based statistical ma- chine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based …...

Current system combination methods usu- ally use confusion networks to find consensus translations among different systems. Requir- ing one-to-one mappings between the words in candidate translations, confusion networks have difficulty …...

As a paratactic language, sentence-level argument extraction in Chinese suffers much from the frequent occurrence of ellipsis with regard to inter-sentence arguments. To resolve such problem, this paper proposes a …...

In natural language question answering (QA) systems, questions often contain terms and phrases that are critically important for re- trieving or finding answers from documents. We present a learnable system …...

In this paper, we address the problem of identifying relevant product aspects in a collection of online customer reviews. Be- ing able to detect such aspects represents an important subtask …...

We present an approach to Arabic auto- matic diacritization that integrates syntac- tic analysis with morphological tagging through improving the prediction of case and state features. Our best system in- …...

This paper presents a new algorithm to automatically solve algebra word prob- lems. Our algorithm solves a word prob- lem via analyzing a hypothesis space con- taining all possible equation …...

Syntactic linearization algorithms take a bag of input words and a set of optional con- straints, and construct an output sentence and its syntactic derivation simultaneously. The search problem is …...

First Story Detection is hard because the most accurate systems become pro- gressively slower with each document processed. We present a novel ap- proach to FSD, which operates in constant …...

We tackle the task of extracting tweets that mention a specific event from all tweets that contain relevant keywords, for which the main challenges include unbalanced positive and negative cases, …...

AbstractLong-distance semantic dependencies are crucial for lexical choice in statistical machine translation. In this paper, we study semantic dependencies between verbs and their arguments by modeling selectional preferences in the …...

In Grammatical Error Correction (GEC), se- quence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are …...

AbstractWith the increasing use of influencing incongruent news headlines for spreading fake news, detecting incongruent news articles has become an important research challenge. Most of the earlier studies on incongruity …...