Natural Language Processing and e-Government: Crime Information Extraction from Heterogeneous Data Sources

Much information that could help solve and prevent crimes is never gathered because the reporting methods available to citizens and law enforcement personnel are not optimal. Detectives do not have sufficient time to interview crime victims and witnesses. Moreover, many victims and witnesses are too scared or embarrassed to report incidents. We are developing an interviewing system that will help collect such information. We report here on one component, the crime information extraction module, which uses natural language processing to extract crime information from police reports, newspaper articles, and victims’ and witnesses’ crime narratives. We tested our approach with two types of document: police and witness narrative reports. Our algorithms extract crime-related information, namely weapons, vehicles, time, people, clothes, and locations. We achieved high precision (96%) and recall (83%) for police narrative reports and comparable precision (93%) but somewhat lower recall (77%) for witness narrative reports. The difference in recall was significant at p \u3c .05. We then used a spell checker to evaluate if this would help with witness narrative processing. We found that both precision (94 %) and recall (79%) improved slightly

Evaluation of information retrieval and text mining tools on automatic named entity extraction. Intelligence and security informatics. Proceedings.

We will report evaluation of Automatic Named Entity Extraction feature of IR tools on Dutch, French, and English text. The aim is to analyze the competency of off-the-shelf information extraction tools in recognizing entity types including person, organization, location, vehicle, time, & currency from unstructured text. Within such an evaluation one can compare the effectiveness of different approaches for identifying named entities.

Crime Information Extraction from Police and Witness Narrative Reports

To solve crimes, investigators often rely on interviews with witnesses, victims, or criminals themselves. The interviews are transcribed and the pertinent data is contained in narrative form. To solve one crime, investigators may need to interview multiple people and then analyze the narrative reports. There are several difficulties with this process: interviewing people is time consuming, the interviews - sometimes conducted by multiple officers - need to be combined, and the resulting information may still be incomplete. For example, victims or witnesses are often too scared or embarrassed to report or prefer to remain anonymous. We are developing an online reporting system that combines natural language processing with insights from the cognitive interview approach to obtain more information from witnesses and victims. We report here on information extraction from police and witness narratives. We achieved high precision, 94% and 96%, and recall, 85% and 90%, for both narrative types

Crime data mining: A general framework and some examples

A general framework for crime data mining that draws on experience gained with the Coplink project at the University of Arizona is presented. By increasing efficiency and reducing errors, this scheme facilitates police work and enables investigators to allocate their time to other valuable tasks.published_or_final_versio

Probabilistic Reference to Suspect or Victim in Nationality Extraction from Unstructured Crime News Documents

There is valuable information in unstructured crime news documents which crime analysts must manually search for. To solve this issue, several information extraction models have been implemented, all of which are capable of being enhanced. This gap has created the motivation to propose an enhanced information extraction model that uses named entity recognition to extract the nationality from crime news documents and coreference resolution to associate the nationality to either the suspect or the victim. After the proposed model extracts the nationality, it references it to the suspect or victim by looking up all of the victim related keywords and the suspect related keywords within the text, and their corresponding distances from the position of the nationality keyword. Based on their total distances, a probability score algorithm decides whether the nationality is more likely to belong to either the victim or the suspect. Two experiments were conducted to evaluate the nationality extractor component and the reference identification component used by the model. The former experiment had achieved 90%, 94%, and 91% for precision, recall, and F-measure values respectively. The latter experiment had achieved 65%, 68%, and 66% for precision, recall, and F-measure respectively. The model had achieved promising results after evaluation. Keywords: information extraction, named entity recognition, coreference resolution, crime domai

Knowledge management and human trafficking: using conceptual knowledge representation, text analytics and open-source data to combat organized crime

Globalization, the ubiquity of mobile communications and the rise of the web have all expanded the environment in which organized criminal entities are conducting their illicit activities, and as a result the environment that law enforcement agencies have to police. This paper triangulates the capability of open-source data analytics, ontological knowledge representation and the wider notion of knowledge management (KM) in order to provide an effective, interdisciplinary means to combat such threats, thus providing law enforcement agencies (LEA’s) with a foundation of competitive advantage over human trafficking and other organized crime

Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)

This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory 2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2015) at the China National Convention Center in Beijing, on July 31st 2015. Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study od storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The workshop aimed at bringing together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future. They form a valuable potential for exploiting news data in an innovative way.JRC.G.2-Global security and crisis managemen

Foxes, hounds, and horses : Who or which?

Writers of English can choose whether to mark a high level of sentience in a nonhuman animal by selecting the word who rather than which. An examination of texts relating to foxhunting on the world wide web showed that, in reference to the nonhuman animals involved in foxhunting, writers were most likely to use who in reference to foxes, and least likely to use it in reference to horses. Those who support foxhunting are more likely to recognize the sentience of the fox than those who oppose foxhunting. This may be because those who enjoy foxhunting present the fox as an active creator of the hunt, and as a worthy opponen