Articles

Most NLP systems make predictions based solely on linguistic (textual or spo- ken) input. We show how to use visual information to make better linguistic pre- dictions. We focus on selectional prefer- ence; specifically, determining the plau- sible noun arguments for particular verb predicates. For each argument noun, we extract …...

In the biomedical domain, many terms are neoclassical compounds (composed of several Greek or Latin roots). The study of their morphology is important for numer- ous applications since it makes it possible to structure, translate, retrieve them effi- ciently... In this paper, we propose an original yet fruitful approach to …...

This paper explores a new approach to help non-expert users with no background in linguistics to add new words to a mono- lingual dictionary in a rule-based machine translation system. Our method aims at choosing the correct paradigm which ex- plains not only the particular surface form introduced by the …...

We combine relational and attributional similarity for the task of identifying instances of semantic relations, such as PRODUCT-PRODUCER and ORIGIN- ENTITY, between nominals in text. We use no pre-existing lexical resources, thus simulating a realistic real-world situation, where the coverage of any such resource is limited. Instead, we mine the …...

Among the motivations to write in Wikipedia given by the current literature there is often coincidence, but none of the studies presents the hypothesis of contributing for the visibil- ity of the own national or language related content. Similar to topical coverage studies, we outline a method which allows collecting …...

In this paper, we demonstrate the portabil- ity of the lexical acquisition (LA) method proposed in Cholakov and van Noord (2010a). Here, LA refers to the acqui- sition of linguistic descriptions for words which are not listed in the lexicon of a given computational grammar, i.e., words which are unknown …...

Automatic authorship attribution, by its nature, is much more advantageous if it is domain (i.e., topic and/or genre) indepen- dent. That is, many real world problems that require authorship attribution may not have in-domain training data readily avail- able. However, most previous work based on machine learning techniques focused only …...

Semantic argument structures are often in- complete in that core arguments are not lo- cally instantiated. However, many of these implicit arguments can be linked to ref- erents in the wider context. In this pa- per we explore a number of linguistically motivated strategies for identifying and re- solving such …...

In this paper we introduce an unsupervised learning approach for WordNet construction. The whole construction method is an Expecta- tion Maximization (EM) approach which uses Princeton WordNet 3.0 (PWN) and a corpus as the data source for unsupervised learning. The proposed method can be used to construct WordNet in any …...

In order to automatically extract opinion holders, we propose to harness the con- texts of prototypical opinion holders, i.e. common nouns, such as experts or an- alysts, that describe particular groups of people whose profession or occupation is to form and express opinions towards spe- cific items. We assess their …...

Multiword expressions (MWEs) and named entities (NEs) exhibit unique and idiosyncratic features, thus, they often pose a problem to NLP systems. In order to facilitate their identification we developed the first corpus of Wikipedia articles in which several types of mul- tiword expressions and named entities are manually annotated at …...

We introduce the problem of detecting En- tity Instantiations, a type of entity relation in which a set of entities is introduced, and either a member or subset of this set is mentioned afterwards. We perform the first, reliable, corpus study of Entity In- stantiations, concentrating on intersenten- tial annotation. …...

This paper presents an empirical study on the influence of singletons on the evalua- tion of coreference resolution systems. We present results on two English data sets used in the SEMEVAL 2010 shared task 1 and the CONLL 2011 shared task using the scorers of both shared tasks. We show …...

In this paper we present an approach to large-scale coreference resolution for an ample set of human languages, with a par- ticular emphasis on time performance and precision. One of the distinctive features of our approach is the use of a mature multilingual named entity repository (per- sons and organizations) …...

This paper describes a hybrid French - Roma- nian cognate identification module. This mod- ule is used by a lexical alignment system. Our cognate identification method uses lemma- tized, tagged and sentence-aligned parallel corpora. This method combines statistical techniques, linguistic information (lemmas, POS tags) and orthographic adjustments. We evaluate our …...

Lexical Resources are a critical component for Natural Language Processing applications. However, the high cost of comparing and merg- ing different resources has been a bottleneck to have richer resources with a broad range of po- tential uses for a significant number of lan- guages. With the objective of reducing …...

We present an approach to perform exter- nal plagiarism analysis by applying sev- eral similarity detection techniques, such as lexical measures and a textual entail- ment recognition system developed by our research group. Some of the least expen- sive features of this system are applied to all corpus documents to …...

In this paper we concentrate on the resolution of the semantic ambiguity that arises when a given word has several meanings. This specific task is commonly referred to as Word Sense Disambiguation (WSD). We propose a method that obtains the appropriate senses from a multidimensional analysis (using Relevant Semantic Trees). …...

Although several studies have developed models and type hierarchies for named en- tity annotation, no such resource is avail- able for semantic relation annotation, de- spite its utility for various applications (e.g. question answering, information ex- traction). In this paper, we show that there are two issues in semantic relation …...

We present an approach for Seman- tic Role Labeling (SRL) using Condi- tional Random Fields in a joint identifi- cation/classification step. The approach is based on shallow syntactic information (chunks) and a number of lexicalized fea- tures such as selectional preferences and automatically inferred similar words, ex- tracted using lexical …...