Articles

The problem addressed in this paper is to predict a user’s numeric rating in a prod- uct review from the text of the review. Un- igram and n-gram representations of text are common choices in opinion mining. However, unigrams cannot capture impor- tant expressions like “could have been bet- ter”, …...

In practical applications, decoding speed is very important. Modern structured learning technique adopts template based method to extract millions of features. Complicated templates bring about abun- dant features which lead to higher accu- racy but more feature extraction time. We propose Two Dimensional Trie (2D Trie), a novel efficient feature …...

We use web-scale N-grams in a base NP parser that correctly analyzes 95.4% of the base NPs in natural text. Web-scale data improves performance. That is, there is no data like more data. Performance scales log-linearly with the number of parame- ters in the model (the number of unique N-grams). …...

Although much work in NLP has focused on simply determining what a document means, we also must know whether or not to believe it. Fact-finding algorithms at- tempt to identify the “truth” among com- peting claims in a corpus, but fail to take advantage of the user’s prior knowl- edge …...

This paper presents an approach to sum- marize single scientific papers, by extract- ing its contributions from the set of cita- tion sentences written in other papers. Our methodology is based on extracting sig- nificant keyphrases from the set of cita- tion sentences and using these keyphrases to build the …...

Text summarization solves the problem of extracting important information from huge amount of text data. There are vari- ous methods in the literature that aim to find out well-formed summaries. One of the most commonly used methods is the Latent Semantic Analysis (LSA). In this paper, different LSA based summariza- …...

During face-to-face conversation, people naturally integrate speech, gestures and higher level language interpretations to predict the right time to start talking or to give backchannel feedback. In this paper we introduce a new model called Latent Mixture of Discriminative Experts which addresses some of the key issues with multimodal language …...

This paper proposes a co-training style algorithm called Co-STAR that acquires hyponymy relations simultaneously from structured and unstructured text. In Co- STAR, two independent processes for hy- ponymy relation acquisition – one han- dling structured text and the other han- dling unstructured text – collaborate by re- peatedly exchanging the …...

We evaluate two dependency parsers, MSTParser and MaltParser, with respect to their capacity to recover unbounded de- pendencies in English, a type of evalu- ation that has been applied to grammar- based parsers and statistical phrase struc- ture parsers but not to dependency parsers. The evaluation shows that when combined …...

This paper describes a search procedure to discover optimal feature sets for depen- dency parsers. The search applies to the shift–reduce algorithm and the feature sets are extracted from the parser configura- tion. The initial feature is limited to the first word in the input queue. Then, the procedure uses …...

We present an unsupervised word seg- mentation model for machine translation. The model uses existing monolingual seg- mentation techniques and models the joint distribution over source sentence segmen- tations and alignments to the target sen- tence. During inference, the monolin- gual segmentation model and the bilin- gual word alignment model …...

The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM sys- tem relies on the compositionality prin- ciple and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our me- thod on 1000 sentences, …...

In this paper we explore the possibility of using cross lingual projections that help to automatically induce role-semantic annotations in the PropBank paradigm for Urdu, a resource poor language. This technique provides annotation projections based on word alignments. It is relatively inexpensive and has the potential to re- duce human …...

Relations between entities in text have been widely researched in the natu- ral language processing and information- extraction communities. The region con- necting a pair of entities (in a parsed sentence) is often used to construct ker- nels or feature vectors that can recognize and extract interesting relations. Such re- …...

This paper presents a simple and effi- cient algorithm for approximate dictio- nary matching designed for similarity measures such as cosine, Dice, Jaccard, and overlap coefficients. We propose this algorithm, called CPMerge, for the τ- overlap join of inverted lists. First we show that this task is solvable exactly by …...

The detailed analyses of sentence struc- ture provided by parsers have been applied to address several information extraction tasks. In a recent bio-molecular event ex- traction task, state-of-the-art performance was achieved by systems building specif- ically on dependency representations of parser output. While intrinsic evalua- tions have shown significant advances …...

Texts are commonly interpreted based on the entire discourse in which they are sit- uated. Discourse processing has been shown useful for inference-based applica- tion; yet, most systems for textual entail- ment – a generic paradigm for applied in- ference – have only addressed discourse considerations via off-the-shelf corefer- ence …...

This paper investigates how to automat- ically create a dialogue control compo- nent of a listening agent to reduce the cur- rent high cost of manually creating such components. We collected a large number of listening-oriented dialogues with their user satisfaction ratings and used them to create a dialogue control …...

We propose to analyse semantic similar- ity in comparable text by matching syn- tactic trees and labeling the alignments according to one of five semantic simi- larity relations. We present a Memory- based Graph Matcher (MBGM) that per- forms both tasks simultaneously as a com- bination of exhaustive pairwise classifica- …...

This paper proposes a multi-label ap- proach to detect emotion causes. The multi-label model not only detects mul- ti-clause causes, but also captures the long-distance information to facilitate emotion cause detection. In addition, based on the linguistic analysis, we cre- ate two sets of linguistic patterns during feature extraction. Both …...