Статьи

Online learning algorithms like the percep- tron are widely used for structured predic- tion tasks. For sequential search problems, like left-to-right tagging and parsing, beam search has been successfully combined with perceptron variants that accommodate search errors (Collins and Roark, 2004; Huang et al., 2012). However, perceptron training with inexact …...

Multi-Domain learning (MDL) assumes that the domain labels in the dataset are known. However, when there are multiple metadata at- tributes available, it is not always straightfor- ward to select a single best attribute for do- main partition, and it is possible that combin- ing more than one metadata attributes …...

Extensive experiments have validated the ef- fectiveness of the corpus-based method for classifying the word’s sentiment polarity. However, no work is done for comparing d- ifferent corpora in the polarity classification task. Nowadays, Twitter has aggregated huge amount of data that are full of people’s senti- ments. In this paper, …...

Matrix and tensor factorization have been ap- plied to a number of semantic relatedness tasks, including paraphrase identification. The key idea is that similarity in the latent space implies semantic relatedness. We describe three ways in which labeled data can im- prove the accuracy of these approaches on paraphrase classification. …...

Linking name mentions in microblog posts to a knowledge base, namely microblog entity linking, is useful for text mining tasks on mi- croblog. Entity linking in long text has been well studied in previous works. However few work has focused on short text such as mi- croblog post. Microblog posts …...

MIRA based tuning methods have been widely used in statistical machine translation (SMT) system with a large number of fea- tures. Since the corpus-level BLEU is not de- composable, these MIRA approaches usually define a variety of heuristic-driven sentence- level BLEUs in their model losses. Instead, we present a new …...

Multilingual speakers switch between lan- guages in online and spoken communication. Analyses of large scale multilingual data re- quire automatic language identification at the word level. For our experiments with mul- tilingual online discussions, we first tag the language of individual words using language models and dictionaries. Secondly, we incor- …...

Most of the machine translation systems rely on a large set of translation rules. These rules are treated as discrete and independent events. In this short paper, we propose a novel method to model rules as observed generation output of a compact hidden model, which leads to better generalization capability. …...

Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a gen- uine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently pro- posed method which aims to improve KB cov- erage by performing inference directly over the KB graph. For the first time, we demon- …...

We present an approach to time normalization (e.g. the day before yesterday⇒2013-04-12) based on a synchronous context free grammar. Synchronous rules map the source language to formally defined operators for manipulat- ing times (FINDENCLOSED, STARTATENDOF, etc.). Time expressions are then parsed using an extended CYK+ algorithm, and converted to a …...

The rise of “Big Data” analytics over unstruc- tured text has led to renewed interest in infor- mation extraction (IE). We surveyed the land- scape of IE technologies and identified a major disconnect between industry and academia: while rule-based IE dominates the commercial world, it is widely regarded as dead-end …...

Neural network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking n-best translations. However, CSLMs have not been used in the first pass decoding of SMT, because using CSLMs in decoding takes a lot …...

Recognizing bridging anaphora is difficult due to the wide variation within the phenomenon, the resulting lack of easily identifiable surface markers and their relative rarity. We develop linguistically motivated discourse structure, lexico-semantic and genericity detection fea- tures and integrate these into a cascaded mi- nority preference algorithm that models bridg- …...

This paper explores to what extent lemmati- sation, lexical resources, distributional seman- tics and paraphrases can increase the accuracy of supervised models for dialogue manage- ment. The results suggest that each of these factors can help improve performance but that the impact will vary depending on their com- bination and …...

State-of-the-art systems for grammatical er- ror correction are based on a collection of independently-trained models for specific er- rors. Such models ignore linguistic interac- tions at the sentence level and thus do poorly on mistakes that involve grammatical depen- dencies among several words. In this paper, we identify linguistic structures …...

This paper presents DefMiner, a supervised sequence labeling system that identifies scien- tific terms and their accompanying definitions. DefMiner achieves 85% F1 on a Wikipedia benchmark corpus, significantly improving the previous state-of-the-art by 8%. We exploit DefMiner to process the ACL An- thology Reference Corpus (ARC) – a large, real-world …...

The problem of learning language models from large text corpora has been widely stud- ied within the computational linguistic com- munity. However, little is known about the performance of these language models when applied to the computer vision domain. In this work, we compare representative models: a window-based model, a …...

We present the first provably optimal polyno- mial time dynamic programming (DP) algo- rithm for best-first shift-reduce parsing, which applies the DP idea of Huang and Sagae (2010) to the best-first parser of Sagae and Lavie (2006) in a non-trivial way, reducing the complexity of the latter from exponential to …...

We present a method which exploits auto- matically generated scientific discourse an- notations to create a content model for the summarisation of scientific articles. Full pa- pers are first automatically annotated using the CoreSC scheme, which captures 11 content- based concepts such as Hypothesis, Result, Conclusion etc at the sentence …...

Document summarization is an important task in the area of natural language processing, which aims to extract the most important in- formation from a single document or a clus- ter of documents. In various summarization tasks, the summary length is manually de- fined. However, how to find the proper sum- …...