Sentiment and behaviour annotation in a corpus of dialogue summaries

This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by focusing on one of the many aspects of sentiment: sentiment as it is recorded in behaviour reports of people and their interactions. Together with a number of measures for supporting the reliable application of the scheme, this allows us to obtain sufficient to good agreement scores (in terms of Krippendorf's alpha) on three key dimensions: polarity, evaluated party and type of clause. Evaluation of the scheme is carried out through the annotation of an existing corpus of dialogue summaries (in English and Portuguese) by nine annotators. Our contribution to the field is twofold: (i) a reliable multi-dimensional annotation scheme for sentiment in behaviour reports; and (ii) an annotated corpus that was used for testing the reliability of the scheme and which is made available to the research community

Narrativizing Knowledge Graphs

Any natural language expression of a set of facts - that can be represented as a knowledge graph - will more or less overtly assume a specific perspective on these facts. In this paper we see the conversion of a given knowledge graph into natural language as the construction of a narrative about the assertions made by the knowledge graph. We, therefore, propose a specific pipeline that can be applied to produce linguistic narratives from knowledge graphs using an ontological layer and corresponding rules that turn a knowledge graph into a semantic specification for natural language generation. Critically, narratives are seen as necessarily committing to specific perspectives taken on the facts presented. We show how this most commonly neglected facet of producing summaries of facts can be brought under control

GenLeNa: Sistema para la construcción de Aplicaciones de Generación de Lenguaje Natural

In this article the proposal is made for the division of the process of construction of natural language generation (NLG) systems into two stages: content planning (CP), which is dependent on the mastery of the application to be developed, and document structuring (DS). This division allows people who are not expert in NLG to develop natural language generation systems, concentrating on building abstract representations of the information to be communicated (called messages). Specific architecture for the DS stage is also presented. This enables NLG researchers to work ortogonally on specific techniques and methodologies for the conversion of messages into text which is grammatically and syntactically correct.En este artículo se propone la división del proceso de construcción de sistemas de Generación de Lenguajes Natural (GLN) en dos etapas: planificación del contenido (EPC), que es dependiente del dominio de la aplicación a desarrollar, y estructuración del documento (EED). Esta división permite que personas no expertas en GLN puedan desarrollar sistemas de generación de lenguajes natural enfocándose en construir representaciones abstractas de la información que se desea comunicar (denominadas mensajes). Adicionalmente se presenta una arquitectura específica para la etapa EED que permite a investigadores en GLN trabajar ortogonalmente en técnicas y metodologías específicas para la transformación de los mensajes en texto gramatical y sintácticamente correcto

Perjury: Establishing a Better Understanding of the Forgotten Crime

Research in the forensic psychology field has primarily focused on eye-witness testimony. Although eye-witness testimony is an important topic to review, it leaves many issues in the legal system, such as perjury, to rely on precedence. Perjury is the willful presentation of false information as truth. Perjury is deception within the legal system, and research indicates that legal personnel, including police officers, detective, and secret service agents, detect deception slightly above chance levels. Little research has been done to determine how often and why perjury occurs. Two studies were conducted to address the rate and incentive of perjury. Study one was an online survey which measured perjury frequencies. Study two was designed to replicate perjury-like behaviors. The results indicated that perjury occurs in 29.9% of the interactions with legal personnel. Individuals who received money as an incentive and individuals who did not were equally likely to engage in perjury behaviors

Supporting process model validation through natural language generation

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation

Artificial Speech and Its Authors

Some of the systems used in natural language generation (NLG), a branch of applied computational linguistics, have the capacity to create or assemble somewhat original messages adapted to new contexts. In this paper, taking Bernard Williams’ account of assertion by machines as a starting point, I argue that NLG systems meet the criteria for being speech actants to a substantial degree. They are capable of authoring original messages, and can even simulate illocutionary force and speaker meaning. Background intelligence embedded in their datasets enhances these speech capacities. Although there is an open question about who is ultimately responsible for their speech, if anybody, we can settle this question by using the notion of proxy speech, in which responsibility for artificial speech acts is assigned legally or conventionally to an entity separate from the speech actant

Implied Feedback: Learning Nuances of User Behavior in Image Search

User feedback helps an image search system refine its relevance predictions, tailoring the search towards the user’s preferences. Existing methods simply take feedback at face value: clicking on an image means the user wants things like it; commenting that an image lacks a specific attribute means the user wants things that have it. How-ever, we expect there is actually more information behind the user’s literal feedback. In particular, a user’s (possibly subconscious) search strategy leads him to comment on cer-tain images rather than others, based on how any of the vis-ible candidate images compare to the desired content. For example, he may be more likely to give negative feedback on an irrelevant image that is relatively close to his target, as opposed to bothering with one that is altogether different. We introduce novel features to capitalize on such implied feedback cues, and learn a ranking function that uses them to improve the system’s relevance estimates. We validate the approach with real users searching for shoes, faces, or scenes using two different modes of feedback: binary rele-vance feedback and relative attributes-based feedback. The results show that retrieval improves significantly when the system accounts for the learned behaviors. We show that the nuances learned are domain-invariant, and useful for both generic user-independent search as well as personal-ized user-specific search. 1