There are several MT metrics used to evaluate translation into Spanish, although most of them use partial or little linguistic information. In this paper we present the multilingual capability of VERTa, an automatic MT metric that combines linguistic information at lexical, morphological, syntactic and semantic level. In the experiments conducted we aim at identifying those linguistic features that prove the most effective to evaluate adequacy in Spanish segments. This linguistic information is tested both as independent modules (to observe what each type of feature provides) and in a combinatory fastion (where different kinds of information interact with each other). This allows us to extract the optimal combination. In addition we compare these linguistic features to those used in previous versions of VERTa aimed at evaluating adequacy for English segments. Finally, experiments show that VERTa can be easily adapted to other languages than English and that its collaborative approach correlates better with human judgements on adequacy than other well-known metrics.

In the last decades, a wide range of automatic metrics that use linguistic knowledge has been developed. Some of them are based on lexical information, such as METEOR; others rely on the use of syntax, either using constituent or dependency analysis; and others use semantic information, such as Named Entities and semantic roles. All these metrics work at a specific linguistic level, but some researchers have tried to combine linguistic information, either by combining several metrics following a machine-learning approach or focusing on the combination of a wide variety of metrics in a simple and straightforward way. However, little research has been conducted on how to combine linguistic features from a linguistic point of view. In this paper we present VERTa, a metric which aims at using and combining a wide variety of linguistic features at lexical, morphological, syntactic and semantic level. We provide a description of the metric and report some preliminary experiments which will help us to discuss the use and combination of certain linguistic features in order to improve the metric performance

FreeLing is an open-source multilingual language processing library providing a wide range of language analyzers for several languages. It offers text processing and language annotation facilities to natural language processing application developers, simplifying the task of building those applications. FreeLing is customizable and extensible. Developers can use the default linguistic resources (dictionaries, lexicons, grammars, etc.) directly, or extend them, adapt them to specific domains, or even develop new ones for specific languages. This paper overviews the recent history of this tool, summarizes the improvements and extensions incorporated in the latest version, and depicts the architecture of the library. Special focus is brought to the fact and consequences of the library being open-source: After five years and over 35,000 downloads, a growing user community has extended the initial threelanguages (English, Spanish and Catalan) to eight (adding Galician, Italian, Welsh, Portuguese, and Asturian), proving that the collaborative open model is a productive approach for the development of NLP tools and resources.

This paper presents the development of an open-source Spanish Dependency Grammar implemented in FreeLing environment. This grammar was designed as a resource for NLP applications that require a step further in natural language automatic analysis, as is the case of Spanish-to-Basque translation. The development of wide-coverage rule-based grammars using linguistic knowledge contributes to extend the existing Spanish deep parsers collection, which sometimes is limited. Spanish FreeLing Dependency Grammar, named EsTxala, provides deep and robust parse trees, solving attachments for any structure and assigning syntactic functions to dependencies. These steps are dealt with hand-written rules based on linguistic knowledge. As a result, FreeLing Dependency Parser gives a unique analysis as a dependency tree for each sentence analyzed. Since it is a resource open to the scientific community, exhaustive grammar evaluation is being done to determine its accuracy as well as strategies for its manteinance and improvement. In this paper, we show the results of an experimental evaluation carried out over EsTxala in order to test our evaluation methodology.

We present the results of an agreement task carried out in the framework of the KNOW Project and consisting in manually annotating an agreement sample totaling 50 sentences extracted from the SenSem corpus. Diambiguation was carried out for all nouns, proper nouns and adjectives in the sample, all of which were assigned EuroWordNet (EWN) synsets. As a result of the task, Spanish WN has been shown to exhibit 1) lack of explanatory clarity (it does not define word meanings, but glosses and examplifies them instead; it does not systematically encode metaphoric meanings, either); 2) structural inadequacy (some words appear as hyponyms of another sense of the same word; sometimes there even coexist in Spanish WN a general sense and a specific one related to the same concept, but with no structural link in between; hyperonymy relationships have been detected that are likely to raise doubts to human annotators; there can even be found cases of auto-hyponymy); 3) cross-linguistic inconsistency (there exist in English EWN concepts whose lexical equivalent is missing in Spanish WN; glosses in one language more often than not contradict or diverge from glosses in another language).

The primary aim of the project SENSEM (Sentence Semantics, BFF2003-06456) is the construction of a Lexical Data Base illustrating the syntactic and semantic behavior of each of the senses of the 250 most frequent verbs of Spanish. With this objective in mind, we are currently building an annotated corpus consisting of sentences extracted from the electronic version of the newspaper El Periódico de Catalunya, totalling approximately 1 million words, with 100 examples of each verb. By the time of the conference, we will be about to complete the annotation of 25,000 sentences, which means roughly a corpus of 800,000 words. Approximately 400,000 of them will have been revised. We expect to make the corpus publicly available by the end of 2006.

We present a first approach to the application of a data mining technique, Multiple Sequence Alignment, to the systematization of a polemic aspect of discourse, namely, the expression of contrast, concession, counterargument and semantically similar discursive relations. The representation of the phenomena under study is carried out by very simple techniques, mostly pattern-matching, but the results allow to drive insightful conclusions on the organization of this aspect of discourse: equivalence classes of discourse markers are established, and systematic patterns are discovered, which will be applied in enhancing a discursive parser.

We present a machine translation framework in which the interlingua— Lexical Conceptual Structure (LCS)—is coupled with a definitional component that includes bilingual (EuroWordNet) links between words in the source and target languages. While the links between individual words are language-specific, the LCS is designed to be a language-independent, compositional representation. We take the view that the two types of information—shallower, transfer-like knowledge as well as deeper, compositional knowledge—can be reconciled in interlingual machine translation, the former for overcoming the intractability of LCS-based lexical selec- tion, and the latter for relating the underlying semantics of two words cross-linguistically. We describe the acquisition process for these two information types and present results of hand-verification of the acquired lexicon. Finally, we demonstrate the utility of the two information types in interlingual MT.