tagging to the corresponding domain. Previous annotation levels as lemmatization and word segmentation but also named entities are required. The goal in identifying domain specific phrases in the documents is the same as for the named entitiy recognition, *i.e.* to identify the relevant semantic units. Even if previous experiments (see (Lewis, 1992) among others) have shown a little impact of the phrases on IR performance, we argue that terminology should have a more significant impact on specialized search engines, as a terminology is relevant for a specific domain. In addition to that, a normalization procedure can associate a canonical form to any phrase occurrence (e.g. gene expression, expression of gene, gene *expressed*…). This normalization step is similar to the lemmatization one for words. Gathering associated variants under a single form modifies the phrase frequencies and thus affects IR.

## Parsing

The parsing module aims at exhibiting the graph of the syntactic dependency relations between the words of the sentence. Parsing is a time and resource-consuming NLP, especially when compared to other NLP tasks like named entity recognition or part-of-speech tagging. As mentioned above, the syntactic analysis is especially important for the tasks that involve relations between entities (either information extraction or relational queries such as X's speeches as opposed to speeches on or *relative* to X). However, this technology is not yet fully compatible with Information Retrieval or Extraction. Even if processing time is a critical point for syntactic parsing, we argue that it may enhance the semantic access to web documents. On the one hand, it is usually not necessary to parse the entire documents. A good filtering procedure may select the more relevant sections to parse. We still have to develop a method for pre-filtering the textual segments that are worth parsing as proposed in (Nédellec et al., 2001). On the other hand, as we will show in Section 5, a good recognition of the terms can reduce significantly the number of possible parses and consequently the parsing processing time. In Ogmios, the word level of annotation is required in the parser input. Depending on the choice of the parser, the morpho-syntactic level may be needed. The Link Grammar Parser (Sleator & Temperley, 1993) is integrated.

## Semantic Type Tagging And Anaphora Resolution

The last modules are currently under test and should be integrated in the next release of the platform. The semantic type tagging associates to the previously identified semantic units tags referring to ontological concepts. This allows a semantic querying of the document base. The anaphora resolution module establishes coreference links between the anaphoric pronoun occurrences and the antecedents they refer to. Even if solving anaphora has a small impact on the frequency counts and therefore on IE, it increases IE recall: for instance it *inhibits* Y may stand for X *inhibits* Y and must be interpreted as such in a extraction engine dealing with gene interactions.

## 5. Performance Analysis

We carried out an experiment on a collection of 55,329 web documents from the biological domain. All the documents went through all NLP modules, up to the term tagging (as mentioned before, the goal is not to parse the whole documents but only some filtered part of them). A 400,000 named entity list, including species and gene names, and a 375,000 term list, issued from the MeSH and Gene Ontology have been used.