Eric Kergosien

Are opinions expressed in land-use planning documents?

A great deal of research on information extraction from textual datasets has been performed in specific data contexts, such as movie reviews, commercial product evaluations, campaign speeches, etc. In this paper, we raise the question on how appropriate these methods are for documents related to land-use planning. The kind of information sought concerns the stakeholders, sentiments, geographic information, and everything else related to the territory. However, it is extremely challenging to link sentiments to the three dimensions that constitute geographic information (location, time, and theme). After highlighting the limitations of existing proposals and discussing issues related to textual data, we present a method called OPILAND (OPinion mIning from LAND-use planning documents) designed to semi-automatically mine opinions related to named-entities in specialized contexts. Experiments are conducted on a Thau lagoon dataset (France), and then applied on three datasets that are related to different areas in order to highlight the relevance and the broader applications of our proposal.

Text2Geo: from textual data to geospatial information

In this paper, we focus on methods for extracting spatial information in text documents. After presenting textual description of space and manual annotation of named entities, mainly location and organization, we present our proposal Text2Geo. It is a hybrid method which combines information extraction approach based on patterns with a supervised classification approach to explore context. We discuss some results obtained on the dataset of Thau lagoon.

Extracting new spatial entities and relations from short messages

In the past few years, texts have become an important spatial data resource, in addition to maps, satellite images and GPS. Electronic written texts used in mediated interactions, especially short messages (SMS, tweets, etc.), have triggered the emergence of new ways of writing. Extracting information from such short messages, which represent a rich source of information and opinion, is highly important due to the new and challenging text style. Short messages are, however, difficult to analyze because of their brief, unstructured and informal nature. The work presented in this paper is aimed at extracting spatial information from two authentic corpora of SMS and tweets in French in order to take advantage of the vast amount of geographical knowledge expressed in diverse natural language texts. We propose a process in which, firstly, we extract new spatial entities (e.g. Monpelier, Montpel are associated with the place name Montpellier). Secondly, we identify new spatial relations that precede these spatial entities (e.g. sur, par, etc.). Finally, we propose a general pattern for discovering spatial relations (e.g. SR+ Preposition). The task is very challenging and complex due to the specificity of short messages language, which is based on weakly standardized modes of writing (lexical creation, massive use of abbreviations, textual variants, etc.). The experiments that were carried out on the two corpora 88milSMS and Tweets highlight the efficiency of our proposed strategy for identifying new kinds of spatial entities and relations.

Discovering Types of Spatial Relations with a Text Mining Approach

Knowledge discovery from texts, particularly the identification of spatial information is a difficult task due to the complexity of texts written in natural language. Here we propose a method combining two statistical approaches (lexical and contextual analysis) and a text mining approach to automatically identify types of spatial relations. Experiments conducted on an English corpus are presented.

Spatial Information Extraction from Short Messages

Abstract Texts in addition to maps and satellite images, have become an important spatial data resource in recent years. Electronic written texts used in mediated interactions, especially short messages, have triggered the emergence of new ways of writing. Extracting information from such short messages, which represent a rich source of information, is highly important in order to discover domain-relevant information in the text and facilitate information retrieval. However, short messages are hard to analyse because of their brief, unstructured and informal nature. This paper focuses on the kinds of special or unique spatial entities and relations are contained in short messages. A new entity extraction method specifically dedicated to French short messages (SMS and tweets) is outlined to address this issue. The method is then tested on more traditional sources, like newspaper texts. This work is crucial in order to take advantage of the vast amount of geographical knowledge expressed in heterogeneous unstructured data. Firstly, we propose a process in which new spatial entities are extracted (e.g. motpellier, montpelier, Montpel are associated with Montpellier). Secondly, we identify new spatial relations that precede spatial entities (e.g. sur, par). Finally, we propose general patterns for the extraction of spatial relations. The task is very challenging and complex due to the specificity of short message language, which is based on weakly standardized modes of writing. The experiments were carried out on the three French corpora (i.e. 88milSMS, tweets, and Midi Libre) and highlight the efficiency of our proposal for identifying new kinds of spatial entities and relations.

When textual information becomes spatial information compatible with satellite images

With the amount of textual data available on the web, new methodologies of knowledge extraction domain are provided. Some original methods allow the users to combine different types of data in order to extract relevant information. In this context, we present the cornerstone of manipulations on textual documents and their preparation for extracting compatible spatial information with those contained in satellite images. The term footprint is defined and its extraction is performed. In this paper, we describe the general process and some experiments conducted in the ANIMITEX project, which aims to match the information coming from texts with those of satellite images.

Looking for Opinion in Land-Use Planning Corpora

A great deal of research on opinion mining and sentiment analysis has been done in specific contexts such as movie reviews, commercial evaluations, campaign speeches, etc. In this paper, we raise the issue of how appropriate these methods are for documents related to land-use planning. After highlighting limitations of existing proposals and discussing issues related to textual data, we present the method called Opiland OPinion mIning from LAND-use planning documents designed to semi-automatically mine opinions in specialized contexts. Experiments are conducted on a land-use planning dataset, and on three datasets related to others areas highlighting the relevance of our proposal.

Senterritoire for Spatio-Temporal Representation of Opinions

In previous work, he method called Opiland (OPinion mIning from LAND-use planning documents) has been proposed in order to semi-automatically mine opinions in specialized contexts. In this article, we present the associated Senterritoire viewer developed to dynamically represent, in time and space, opinions extracted by Opiland.