Articles

In this paper, we present a simple and effective method to address the issue of how to generate diversified translation systems from a single Statistical Machine Translation (SMT) engine for system combination. Our method is based on the framework of boosting. First, a se- quence of weak translation systems is …...

We outline different methods to detect er- rors in automatically-parsed dependency corpora, by comparing so-called depen- dency rules to their representation in the training data and flagging anomalous ones. By comparing each new rule to every rel- evant rule from training, we can identify parts of parse trees which are …...

One of the main obstacles to produc- ing high quality joint models is the lack of jointly annotated data. Joint model- ing of multiple natural language process- ing tasks outperforms single-task models learned from the same data, but still under- performs compared to single-task models learned on the more abundant …...

Syntactic knowledge is important for dis- course relation recognition. Yet only heu- ristically selected flat paths and 2-level production rules have been used to incor- porate such information so far. In this paper we propose using tree kernel based approach to automatically mine the syn- tactic information from the parse …...

We present a game-theoretic model of bar- gaining over a metaphor in the context of political communication, find its equilib- rium, and use it to rationalize observed linguistic behavior. We argue that game theory is well suited for modeling dis- course as a dynamic resulting from a num- ber of …...

Automatic processing of metaphor can be clearly divided into two subtasks: metaphor recognition (distinguishing be- tween literal and metaphorical language in a text) and metaphor interpretation (iden- tifying the intended literal meaning of a metaphorical expression). Both of them have been repeatedly addressed in NLP. This paper is the first …...

The automatic interpretation of noun-noun compounds is an important subproblem within many natural language processing applications and is an area of increasing interest. The problem is difficult, with dis- agreement regarding the number and na- ture of the relations, low inter-annotator agreement, and limited annotated data. In this paper, we …...

We describe the semi-automatic adapta- tion of a TimeML annotated corpus from English to Portuguese, a language for which TimeML annotated data was not available yet. In order to validate this adaptation, we use the obtained data to replicate some results in the literature that used the original English data. …...

Comparing one thing with another is a typical part of human decision making process. How- ever, it is not always easy to know what to compare and what are the alternatives. To ad- dress this difficulty, we present a novel way to automatically mine comparable entities from comparative questions that …...

In this paper, we propose a novel approach to automatic generation of summary tem- plates from given collections of summary articles. This kind of summary templates can be useful in various applications. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then …...

We observe that (1) how a given named en- tity (NE) is translated (i.e., either semanti- cally or phonetically) depends greatly on its associated entity type, and (2) entities within an aligned pair should share the same type. Also, (3) those initially detected NEs are an- chors, whose information should …...

We propose a translation recommendation framework to integrate Statistical Machine Translation (SMT) output with Transla- tion Memory (TM) systems. The frame- work recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We describe an …...

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 probabilities calculated from N- best lists or word lattices. We propose to incorporate two groups of linguistic fea- tures, which …...

The adoption of Machine Translation tech- nology for commercial applications is hampered by the lack of trust associated with machine-translated output. In this pa- per, we describe TrustRank, an MT sys- tem enhanced with a capability to rank the quality of translation outputs from good to bad. This enables the …...

Subjectivity analysis is a rapidly grow- ing field of study. Along with its ap- plications to various NLP tasks, much work have put efforts into multilingual subjectivity learning from existing re- sources. Multilingual subjectivity analy- sis requires language-independent crite- ria for comparable outcomes across lan- guages. This paper proposes to …...

We present a method for automatically generating focused and accurate topic- specific subjectivity lexicons from a gen- eral purpose polarity lexicon that allow users to pin-point subjective on-topic in- formation in a set of relevant documents. We motivate the need for such lexicons in the field of media analysis, describe …...

In this paper, we introduce a corpus of consumer reviews from the rateitall and the eopinions websites annotated with opinion-related information. We present a two-level annotation scheme. In the first stage, the reviews are analyzed at the sentence level for (i) relevancy to a given topic, and (ii) expressing an …...

In this paper we present a joint con- tent selection and compression model for single-document summarization. The model operates over a phrase-based rep- resentation of the source document which we obtain by merging information from PCFG parse trees and dependency graphs. Using an integer linear programming for- mulation, the model …...

Identifying background (context) informa- tion in scientific articles can help schol- ars understand major contributions in their research area more easily. In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientific papers. We model the sentences in an article and their …...

To date, few attempts have been made to develop and validate methods for au- tomatic evaluation of linguistic quality in text summarization. We present the first systematic assessment of several diverse classes of metrics designed to capture var- ious aspects of well-written text. We train and test linguistic quality models …...