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\title{Working on a botanic corpus}
\author{Eric de la Clergerie}

\begin{document}

\maketitle

\section{Motivation}
\label{sec:motivation}

Extracting information from an encyclopedic corpus of botanic may be
done by hand but it is a long and tedious work. More and more, it
becomes interesting and possible to speed-up the process by
automatizing it but still keeping an human expert for validation.

Among the different kind of information that may be extracted from a
botanic corpus, we can cite terminology, conceptual information to
model a specialized domain (for instance African tropical flora),
and descriptions of a set of plants that follows the conceptual model.

The group ATOLL at INRIA Rocquencourt is interested in this area of
research for different reasons. The group is primarily concerned with
parsing for natural language and we believe that parsing is a key
component for knowledge acquisition tasks from textual documents.
Actually, we are already involved through participations to an ARC RLT
(\footurl{\url{http://atoll.inria.fr/RLT/}}{\emph{Resources
    Linguistiques pour les TAGs}}) and to the working group A3CTE
(\footurl{\url{http://www-lipn.univ-paris13.fr/groupes-de-travail/A3CTE/}}{\emph{Applications,
    Apprentissage, Acquisition de Connaissances à partir de Textes
    Electroniques}}).  For the European project
\footurl{\url{http://www.mda.org.uk/term-it/}}{TermIT}, we also get
some expertise in the domains of the conceptual structures such as
thesaurus, ontologies, and semantic networks, and of the knowledge
representation languages (conceptual graphs, descriptive logics).
Finally, the group has worked on the issues of structuring and
representing documents and linguistic resources (using for instance
XML, and more anciently, using the systems Mentor and Centaur), issues
that are relevant when processing encyclopedic corpus with a strong
underlying structure.

The corpus of flora that we have examined seem to be promising for
these tasks of acquisition. Indeed, they are strongly structured,
following relatively precise patterns of presentation. Style is
relatively formal in some parts (descriptive parts) and freer in
others (explicative parts), allowing to test the dependence of
acquisition w.r.t. style. More over, the corpus present a rich but
very uncommon vocabulary to describe, for instance, colors, textures,
and shapes.  This vocabulary will not be found in available electronic
dictionaries and will require a phase of extraction. Botanic
conceptual models are not trivial but seems relatively well delimited
and organized, having been developped over a long period of time. It
means that a large knowledge can be instilled at the beginning of the
acquisition process by an expert and that validation of extracted
knowledge should be easier.

\section{Program}
\label{sec:program}

We identify 4 main tasks.

\subsection{Structuration of Corpus}
\label{sec:structuration-corpus}

The first step is the transformation of the original unstructured
textual documents into a structured one, better adapted for further
processing.  The aim of the transformation is to delimit (with markup
elements) the different entries and the different components of each
entry (description part, explication part, bibliographic references,
\ldots). The structured document may be encoded in XML, following a
DTD (to be defined) characterizing this class of document.

The task may be achieved using scripts (Perl or Python) based on
regular expressions.

Note that from the structured document may be derived an HTML version,
adequate for on-line browsing. Such a browsable version would be
helpful during the validation phases occurring in the following tasks.

\subsection{Terminological Extraction}
\label{sec:term-acqu}

The first linguistic task consists in identifying the terminology
associated to the corpus and building a list of terms with
morphological variants.

This task may be achieved using scripts, morphological analyzing and
terminological extractors (such as \textbf{FASTER} or
\textbf{LEXTER}). Part-of-speech tagging and /or superficial parsing
may also be needed to find the syntactic category (noun, adjective,
\ldots) of the different terms (because we are dealing with a
vocabulary not necessarily listed in available electronic
dictionaries).

Note that the collected terminology may be used to efficiently index
the corpus and speed-up searches in the on-line version.

\subsection{Conceptual Acquisition}
\label{sec:conc-acqu}

The next task would be the core of the research effort for ATOLL: we
plan to organize the collected terminology into some kind of
conceptual structure (such as a thesaurus, an ontology, or a semantic
network).  The result should model (more or less precisely) the
concepts and relations between these concepts that emerge from the
corpus. For instance, a flower is a kind of plant (\emph{kind-of}
relation) and has different parts (\emph{part-of} relation); each part
has different characteristics such as a color, a texture, a shape, a
dimension, a number, \ldots. A plant has also a geographic
distribution (\emph{location} relation).

The set of relations is very helpful to do inferences, in particular
using heritage through the \emph{kind-of} relation.

Of course, the modeling task may be helped a lot using a general model
explicitly given by botanists. The acquisition process will complete
and specialize this general model w.r.t. the corpus. In particular, it
will assign semantic categories (such as color, shape, texture) to
different terms (mostly adjectives).

This task being more prospective, we only sketch here a few ideas that
should be investigated. We plan to run several passes of partial
parsing and of knowledge acquisition. Each pass of parsing will use
the knowledge already present to be more complete and precise than the
previous one, in particular in resolving ambiguities.  From the
partial parse trees may be collected a set of properties and relations
on concepts that will be forwarded to the knowledge acquisition
module.  This module organizes this set, looking what properties and
relations seems to be pertinent w.r.t. the whole corpus (based for
instance on statistical or ponderation criteria). The emerging
properties and relations may then be proposed to a human supervisor
for validation. A new round of acquisition may then be started.

The efficiency of the parsing passes will be helped by first
identifying the recurrent syntactic and stylistic patterns used in the
corpus. This is most specially true for the entry parts that follow a
quasi controlled pattern.

\subsection{Text Mining}
\label{sec:text-mining}

The last task of the proposition is also a research task but will be
eased by the quality of the conceptual modeling given by the previous
task. The objective is to process each entry in the corpus in order to
fill a database. The processing of an entry will be guided by the
conceptual structure in order to identify where each piece of
information that is found should go in the database. By some aspects,
this task is similar to the previous one, except that the work is done
at the level on an entry rather than on the whole corpus and can no
longer rely on statistical methods to correct errors. Parsing backed
by knowledge and validation by a supervisor will again be used to
achieve this task.

The adequation of language Delta to encode the information extracted
form each entry will be examined.

\section{Objectives}
\label{sec:objectives}

The objective for ATOLL is to explore several research issues during
this work in the area of parsing and acquisition. We also wish to
establish a methodology that may used to deal with other corpus of
encyclopedic information and other domains than botanic. Beside the
research effort, this project will imply the development of prototypes
and, hopefully, the creation of resources (structured corpus,
terminology, conceptual structure, database). It may be noted that,
among of different tools that are needed, one should think to the
validation tools for the human supervisors.

We consider that this project requires a long term commitment of, for
instance, a PhD student to coordinate the different tasks and to
dialog with all the involved communities (botanists and computational
linguists).

\section{Related works}
\label{sec:related-works}

\subsection{WordNet and EuroWordNet}
\label{sec:wordnet-eurowordnet}

WordNet is a \emph{computational lexicon} that classify English words
into sets of synonyms (\emph{synsets}) that denote concepts. Synsets
are themselves organized in a \emph{kind-of} taxonomy. Addtionnal
relations between synsets are present, such as the \emph{part-of}
relations. WordNet has been done by hand, but experiences to
automatize the building of brother wordnets for European languages
(Project EuroWordNet) are being conducted.

The importance of WordNet as a source of conceptual information for
all kinds of linguistic processing has been recognized with many
different experiences and specialized workshops.


\subsection{MindNet}
\label{sec:mindnet}

MindNet is a massive semantic network built by a Microsoft Research
Team by automatically extracting knowledge from the Machine Readable
Dictionary LDOCE and more recently from the encyclopedia Encarta.
Knowledge is extracted using a robust parser based on the grammar
checker of Microsoft Office bundle and incorporated in the semantic
network. The nodes of the network are either concepts (such as
\verb+car+) or relations (such as \verb+drive+) and edges are labeled
by several kinds of lexical, syntactic, thematic or semantic relations
(listed in Figure~\ref{fig:MindNet:Rel}).

\begin{figure}[htbp]
  \begin{center}
    \begin{tabular}{|l|l|l|}
      \hline
      Attribute     & Goal          & Possessor \\ \hline
      Cause         & Hypernym      & Purpose\\ \hline
      Co-Agent      & Location      & Size\\ \hline
      Color         & Manner        & Source\\ \hline
      Deep\_Object  & Material      & Subclass\\ \hline
      Deep\_Subject & Means         & Synonym\\ \hline
      Domain        & Modifier      & Time\\ \hline
      Equivalent    & Part          & User\\ \hline
    \end{tabular}
  \end{center}
  \caption{Semantic relations in MindNet}
  \label{fig:MindNet:Rel}
\end{figure}

\section{Background knowledge at INRIA}

Other INRIA research groups (at Rocquencourt or at the other sites)
may provide useful knowledge or tools in the context of this project,
in particular with representing knowledge and associating documents to
knowledge.

\begin{description}
  
\item[\footurl{\url{http://www.inria.fr/Equipes/VERSO-fra.html}}{Verso}]
  Databases and XML. They are involved in
  \footurl{\url{http://www-rocq.inria.fr/verso/LEVEL1/Xyleme.html}}{XYLEME}
  ``The Data Warehouse for the XML Documents of the WEB'' and with
  \footurl{\url{http://cweb.inria.fr/}}{C-Web} ``Supporting
  Community-Webs''. They already have collaboration with CSIRO.
  
\item[Samie] A very recent group led by Alain Michard, one of the main
  promoter of \footurl{\url{http://cweb.inria.fr/}}{C-Web}.

\end{description}

\nocite{farreres98,ohara98,beeferman98,rigau98,richardson98,kwong98b,miller90,chang96,markowitz86,klavans90,ide93a,alshawi89,gruber93,leacok98,zweingenbaum97,analyti97,dionysiadou94,giacomo97,chodorow85,ahlswede88,montemagni92,vossen97,EWN98,sowa89,sowa91,kayser97,TermIT99}

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