Spatiotemporal Information Extraction from a Historic Expedition Gazetteer

Abstract

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volumes historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners' site visits and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. The reference map was manually prepared in previous research work by others.

Smart job searching system based on information retrieval techniques and similarity of fuzzy parameterized sets

“…In this study, ANNIE sentence splitter is used to split CVs and vacancies into sentences. This splitter uses a gazetteer list of abbreviations to support the process of recognizing sentence-marking full stop [13,22,23]. For example, consider the sentence "Dr. John was born in June 1983"; the full stop after "Dr" is not a sentence-marking stop.…”

“…In this module, tokenization is the way of splitting each sentence into words and terms by removing empty sequences and various symbols such as punctuation, numbers, and symbols in the text. This module uses the ANNIE Tokenizer for tokenizing the text documents and take each word or term from the first character to the last character, where each word or term is called token [22,23].…”

“…This study produces these classes as an annotation class on each token based on predefined rules for categorization utilized through ANNIE POS tagger [23]. Moreover, this tagger extracts each Named Entity such as gender, job title, nationality, location, organization, etc., that can be represented with a proper name [22]. Named entities can simply be viewed as entity instances (e.g., Web designer is an instance of job title).…”

Recognition of Implicit Geographic Movement in Text

“…Therefore, the contributions of our work include 1) a corpus that can be used to a) improve detection of statements of geographic movement, which is currently underused information, b) improve GIR techniques by providing statements about geographic movement with many place mentions, non-geographic contextual information, and linguis-tic aspects describing geographic movement, c) aid spatial cognition researchers in understanding how people communicate and understand geographic movement; and 2) a method to create a corpus of rarely occurring text by bootstrapping human labeling efforts on a small seed set with machine learning predictions to produce a large corpus of high quality. Although other related efforts have improved the use of implicit geographic information in text with route directions (Jaiswal et al, 2010), historical exploration expeditions (Bekele et al, 2016), routes (Drymonas and Pfoser, 2010), hiking route description (Moncla et al, 2014b), other paths (Moncla et al, 2014a), and geospatial natural language (Stock et al, 2013), etc., we believe no existing corpus is as large and diverse with respect to movement types.…”

Automated Geocoding of Textual Documents: A Survey of Current Approaches

“…Within the semantic Web, natural language processing, information retrieval and information extraction communities, much work has been done on extracting facts and relations about geospatial entities, specifically attempting to deal with the fact that geospatial expressions are often vaguely defined and context dependent. This includes studies focused on the extraction and normalization of named places from text (Roberts et al ; Grover et al ; Qin et al ; Gelernter and Mushegian ; Gelernter and Balaji ; Zhang and Gelernter ; Moncla et al ; Santos et al ; Speriosu and Baldridge ; Inkpen et al ; DeLozier et al ; Awamura et al ), studies focusing on geospatial topic modeling (Speriosu et al ; Eisenstein et al ), studies focusing on the extraction of locative expressions beyond named places (Liu et al ; Wallgrün et al ), studies focused on extracting itineraries as described in text (Moncla et al 2015; Bekele ), studies focusing on the extraction of qualitative spatial relations between places (Khan et al ; Wallgrün et al ; Antelman and Cleary ), or studies focusing on the extraction and modeling of general spatial semantics from natural language descriptions (Recchia and Louwerse ; Kordjamshidi et al ).…”