Evaluation of a domain independent approach to natural language processing for game-like user interfaces

Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.

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Dictionaries of Mexican Sexual Slang for NLP

CLEI electronic journal ◽

10.19153/cleiej.20.1.7 ◽

2018 ◽

Author(s):

Roberto Villarejo-Martínez ◽

Noé Alejandro Castro-Sánchez ◽

Gerardo Sierra Martínez

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Human Computer Interaction ◽

Language Processing ◽

User Interfaces ◽

Scientific Community ◽

Recognition Problem ◽

Direct Impact ◽

Mexican Spanish ◽

The Creation

Abstract: In this paper the creation of two relevant resources for the double entendre and humour recognition problem in Mexican Spanish is described: a morphological dictionary and a semantic dictionary. These were created from two sources: a corpus of albures (drawn from “Antología del albur” book) and a Mexican slang dictionary (“El chilangonario”). The morphological dictionary consists of 410 forms of words that corresponds to 350 lemmas. The semantic dictionary consists of 27 synsets that are associated to lemmas of morphological dictionary. Since both resources are based on Freeling library, they are easy to implement for tasks in Natural Language Processing. The motivation for this work comes from the need to address problems such as double entendre and computational humour. The usefulness of these disciplines has been discussed many times and it has been shown that they have a direct impact on user interfaces and, mainly, in human-computer interaction. This work aims to promote that the scientific community generates more resources about informal language in Spanish and other languages. Spanish Abstract: En este artículo se describe la creación de dos recursos relevantes para el reconocimiento del doble sentido y el humor en el español mexicano: un diccionario morfológico y un diccionario semántico. Éstos fueron creados a partir de dos fuentes: un corpus de albures (extraídos del libro "Antología del albur") y un diccionario de argot mexicano ("El chilangonario"). El diccionario morfológico consiste en 410 formas de palabras que corresponden a 350 lemas. El diccionario semántico consiste en 27 synsets que están asociados a lemas del diccionario morfológico. Puesto que ambos recursos están basados en la biblioteca Freeling, son fáciles de implementar en tareas de Procesamiento del Lenguaje Natural. La motivación de este trabajo proviene de la necesidad de abordar problemas como el doble sentido y el humor computacional. La utilidad de estas disciplinas han sido discutidas muchas veces y se ha mostrado que tienen un impacto directo en las interfaces de usuario y, principalmente, en la interacción humano-computadora. Este trabajo tiene como objetivo promover que la comunidad científica genere más recursos sobre el lenguaje informal en español y otros lenguajes.

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Natural Language Processing and Enhanced Clinical Decision Making Radiology and VINCI

PsycEXTRA Dataset ◽

10.1037/e615572012-015 ◽

2012 ◽

Author(s):

Eliot Siegel

Keyword(s):

Decision Making ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Decision Making ◽

Clinical Decision

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Natural Language Processing in the Clinical Setting

PsycEXTRA Dataset ◽

10.1037/e615572012-013 ◽

2012 ◽

Author(s):

Thomas H. Payne

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Setting

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A Review and evaluation of Machine Translation methods for Lumasaaba

Journal of Digital Science ◽

10.33847/2686-8296.2.1_1 ◽

2020 ◽

pp. 3-17

Author(s):

Peter Nabende

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Machine Translation ◽

Language Processing ◽

Research Area ◽

Data Driven ◽

East African ◽

Data Set ◽

African Languages ◽

Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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