Articles

Topical blog post retrieval is the task of rank- ing blog posts with respect to their relevance for a given topic. To improve topical blog post retrieval we incorporate textual credibility in- dicators in the retrieval process. We consider two groups of indicators: post level (deter- mined using information about …...

Expert search, in which given a query a ranked list of experts instead of documents is returned, has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been pro- posed, including metadata extraction, expert profile building, and …...

We propose a cascaded linear model for joint Chinese word segmentation and part- of-speech tagging. With a character-based perceptron as the core, combined with real- valued features such as language models, the cascaded model is able to efficiently uti- lize knowledge sources that are inconvenient to incorporate into the perceptron …...

We present a discriminative structure- prediction model for the letter-to-phoneme task, a crucial step in text-to-speech process- ing. Our method encompasses three tasks that have been previously handled separately: input segmentation, phoneme prediction, and sequence modeling. The key idea is online discriminative training, which updates parameters according to a comparison …...

For Chinese POS tagging, word segmentation is a preliminary step. To avoid error propa- gation and improve segmentation by utilizing POS information, segmentation and tagging can be performed simultaneously. A challenge for this joint approach is the large combined search space, which makes efficient decod- ing very hard. Recent research …...

We identify four types of errors that unsu- pervised induction systems make and study each one in turn. Our contributions include (1) using a meta-model to analyze the incor- rect biases of a model in a systematic way, (2) providing an efficient and robust method of measuring distance between two …...

This paper presents a semi-supervised train- ing method for linear-chain conditional ran- dom fields that makes use of labeled features rather than labeled instances. This is accom- plished by using generalized expectation cri- teria to express a preference for parameter set- tings in which the model’s distribution on un- labeled …...

The traditional mention-pair model for coref- erence resolution cannot capture information beyond mention pairs for both learning and testing. To deal with this problem, we present an expressive entity-mention model that per- forms coreference resolution at an entity level. The model adopts the Inductive Logic Pro- gramming (ILP) algorithm, which …...

This paper explores the relationship between discourse segmentation and coverbal gesture. Introducing the idea of gestural cohesion, we show that coherent topic segments are char- acterized by homogeneous gestural forms and that changes in the distribution of gestural features predict segment boundaries. Gestu- ral features are extracted automatically from video, …...

We extend the classical single-task active learning (AL) approach. In the multi-task ac- tive learning (MTAL) paradigm, we select ex- amples for several annotation tasks rather than for a single one as usually done in the con- text of AL. We introduce two MTAL meta- protocols, alternating selection and rank …...

We define a new formalism, based on Sikkel’s parsing schemata for constituency parsers, that can be used to describe, analyze and com- pare dependency parsing algorithms. This abstraction allows us to establish clear rela- tions between several existing projective de- pendency parsers and prove their correctness. 1...

Different summarization requirements could make the writing of a good summary more dif- ficult, or easier. Summary length and the char- acteristics of the input are such constraints in- fluencing the quality of a potential summary. In this paper we report the results of a quanti- tative analysis on data …...

In this paper, we present a study of a novel summarization problem, i.e., summarizing the impact of a scientific publication. Given a pa- per and its citation context, we study how to extract sentences that can represent the most influential content of the paper. We propose language modeling methods for …...

We describe an unsupervised approach to multi-document sentence-extraction based summarization for the task of producing biographies. We utilize Wikipedia to auto- matically construct a corpus of biographical sentences and TDT4 to construct a corpus of non-biographical sentences. We build a biographical-sentence classifier from these corpora and an SVM regression model …...

There is a widely held belief in the natural lan- guage and computational linguistics commu- nities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and in- formation extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits …...

Paraphrase patterns are useful in paraphrase recognition and generation. In this paper, we present a pivot approach for extracting para- phrase patterns from bilingual parallel cor- pora, whereby the English paraphrase patterns are extracted using the sentences in a for- eign language as pivots. We propose a log- linear model …...

Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled infer- ence and other NLP tasks requiring deep se- mantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related …...

We present a method for learning bilingual translation lexicons from monolingual cor- pora. Word types in each language are charac- terized by purely monolingual features, such as context counts and orthographic substrings. Translations are induced using a generative model based on canonical correlation analy- sis, which explains the monolingual lexicons …...

We address the problem of translating from morphologically poor to morphologically rich languages by adding per-word linguistic in- formation to the source language. We use the syntax of the source sentence to extract information for noun cases and verb persons and annotate the corresponding words accord- ingly. In experiments, we …...

In statistical language modeling, one technique to reduce the problematic effects of data spar- sity is to partition the vocabulary into equiva- lence classes. In this paper we investigate the effects of applying such a technique to higher- order n-gram models trained on large corpora. We introduce a modification of …...