Articles

ion and Generalisation in Semantic Role Labels: PropBank, VerbNet or both? Paola Merlo Linguistics Department University of Geneva 5 Rue de Candolle, 1204 Geneva Switzerland Paola.Merlo@unige.ch Lonneke Van Der Plas Linguistics Department University of Geneva 5 Rue de Candolle, 1204 Geneva Switzerland Lonneke.VanDerPlas@unige.ch Abstract Semantic role labels are the representa- …...

It is usually assumed that the kind of noise existing in annotated data is random clas- sification noise. Yet there is evidence that differences between annotators are not always random attention slips but could result from different biases towards the classification categories, at least for the harder-to-decide cases. Under an …...

This paper presents a novel metric-based framework for the task of automatic taxonomy induction. The framework incrementally clus- ters terms based on ontology metric, a score indicating semantic distance; and transforms the task into a multi-criteria optimization based on minimization of taxonomy structures and modeling of term abstractness. It com- …...

Syntactic analysis influences the way in which the source sentence is translated. Previous efforts add syntactic constraints to phrase-based translation by directly rewarding/punishing a hypothesis when- ever it matches/violates source-side con- stituents. We present a new model that automatically learns syntactic constraints, including but not limited to constituent matching/violation, from …...

Can we automatically compose a large set of Wiktionaries and translation dictionar- ies to yield a massive, multilingual dic- tionary whose coverage is substantially greater than that of any of its constituent dictionaries? The composition of multiple translation dictionaries leads to a transitive inference problem: if word A translates to …...

This paper describes an approach to uti- lizing term weights for sentiment analysis tasks and shows how various term weight- ing schemes improve the performance of sentiment analysis systems. Previously, sentiment analysis was mostly studied un- der data-driven and lexicon-based frame- works. Such work generally exploits tex- tual features for …...

Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possi- ble to perform large-scale mining of …...

The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification. However, there are many freely available English sentiment corpora on the Web. This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chi- nese sentiment classification by using the …...

This paper presents an unsupervised opin- ion analysis method for debate-side clas- sification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associa- tions that are indicative of opinion stances in debates. …...

Computational story telling has sparked great interest in artificial intelligence, partly because of its relevance to educa- tional and gaming applications. Tradition- ally, story generators rely on a large repos- itory of background knowledge contain- ing information about the story plot and its characters. This information is detailed and usually …...

In this paper, we investigate an ap- proach for creating a comprehensive tex- tual overview of a subject composed of in- formation drawn from the Internet. We use the high-level structure of human-authored texts to automatically induce a domain- specific template for the topic structure of a new overview. The …...

Wikipedia provides a wealth of knowl- edge, where the first sentence, infobox (and relevant sentences), and even the en- tire document of a wiki article could be considered as diverse versions of sum- maries (definitions) of the target topic. We explore how to generate a series of summaries with various …...

This paper presents DEPEVAL(summ), a dependency-based metric for automatic evaluation of summaries. Using a rerank- ing parser and a Lexical-Functional Gram- mar (LFG) annotation, we produce a set of dependency triples for each sum- mary. The dependency set for each candidate summary is then automatically compared against dependencies generated from …...

Statistical machine translation (SMT) models require bilingual corpora for train- ing, and these corpora are often multi- lingual with parallel text in multiple lan- guages simultaneously. We introduce an active learning task of adding a new lan- guage to an existing multilingual set of parallel text and constructing high quality …...

This paper proposes a forest-based tree se- quence to string translation model for syntax- based statistical machine translation, which automatically learns tree sequence to string translation rules from word-aligned source- side-parsed bilingual texts. The proposed model leverages on the strengths of both tree sequence-based and forest-based translation models. Therefore, it …...

This paper revisits the pivot language ap- proach for machine translation. First, we investigate three different methods for pivot translation. Then we employ a hybrid method combining RBMT and SMT systems to fill up the data gap for pivot translation, where the source- pivot and pivot-target corpora are inde- pendent. …...

We propose a method to automatically train lemmatization rules that handle prefix, infix and suffix changes to generate the lemma from the full form of a word. We explain how the lemmatization rules are created and how the lemmatizer works. We trained this lemmatizer on Danish, Dutch, English, German, Greek, …...

This paper studies transliteration align- ment, its evaluation metrics and applica- tions. We propose a new evaluation met- ric, alignment entropy, grounded on the information theory, to evaluate the align- ment quality without the need for the gold standard reference and compare the metric with F-score. We study the use …...

Correct stress placement is important in text-to-speech systems, in terms of both the overall accuracy and the naturalness of pronunciation. In this paper, we formu- late stress assignment as a sequence pre- diction problem. We represent words as sequences of substrings, and use the sub- strings as features in a …...

Letter-to-phoneme (L2P) conversion is the process of producing a correct phoneme sequence for a word, given its letters. It is often desirable to reduce the quantity of training data — and hence human anno- tation — that is needed to train an L2P classifier for a new language. In this …...