Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models.

Best practices in justifying calibrations for dating language families

The use of computational methods to assign absolute datings to language divergence is receiving renewed interest, as modern approaches based on Bayesian statistics offer alternatives to the discredited techniques of glottochronology. The datings provided by these new analyses depend crucially on the use of calibration, but the methodological issues surrounding calibration have received comparatively little attention. Especially, underappreciated is the extent to which traditional historical linguistic scholarship can contribute to the calibration process via loanword analysis. Aiming at a wide audience, we provide a detailed discussion of calibration theory and practice, evaluate previously used calibrations, recommend best practices for justifying calibrations, and provide a concrete example of these practices via a detailed derivation of calibrations for the Uralic language family. This article aims to inspire a higher quality of scholarship surrounding all statistical approaches to language dating, and especially closer engagement between practitioners of statistical methods and traditional historical linguists, with the former thinking more carefully about the arguments underlying their calibrations and the latter more clearly identifying results of their work which are relevant to calibration, or even suggesting calibrations directly.

Cooperation, social selection, and language change: An experimental investigation of language divergence

In this thesis, I use an experimental model to investigate the role of social pressures in stimulating language divergence. Research into the evolution of cooperation has emphasised the usefulness of in-group markers for swiftly identifying outsiders, who pose a threat to cooperative networks. Mechanisms for avoiding cheats and freeriders, which tend to rely on reputation, or on (explicit and implicit) contracts between individuals, are considerably less effective against short-term visitors. Outsiders, moreover, may behave according to different social norms, which may adversely affect cooperative interactions with them. There are many sources of markers by which insiders and outsiders can be distinguished, but language is a particularly impressive one.If human beings exploit linguistic variation for this purpose, we might expect the exploitation to have an influence on the cultural evolution of language, and to be involved in language divergence, since it introduces a selective pressure, by which linguistic variants are selected on the basis of their social significance. However, there is also a neutral, mechanistic model of dialect formation that relies on unconscious accommodation between interlocutors, coupled with variation in the frequency of interaction, to account for divergence. In studies of real-world communities, these factors are difficult to tease apart.The model described in this thesis put real speakers in the artificial environment of a computer game. A game consisted of a series of rounds in which players were paired up with each other in a pseudo-random order. During a round, pairs of players exchanged typed messages in a highly restricted artificial “alien language”. Each player began the game with a certain number of points, distributed between various resources, and the purpose of sending messages was to negotiate to exchange these resources. Any points given away were worth double to the receiver, so, by exchanging resources, players could accumulate points for their team. However, the pairings were anonymous: until the end of a round, players were not told who they had been paired with.This basic paradigm allowed the investigation of the major factors influencing language divergence, as well as the small-scale individual strategies that contribute to it. Two major factors were manipulated: frequency of interaction and competitiveness. In one condition, all players in a game were working together; in another condition, players were put into teams, such that giving away resources to team- mates was advantageous, but giving them to opponents was not. This put a pressure on players to use variation in the alien language to mark identity. A combination of this pressure and a minimum level of interaction between teammates was found to be sufficient for the alien language to diverge into “dialects”. Neither factor was sufficient on its own.The results of these experiments suggest that a pressure for the socially based selection of linguistic variants can lead to divergence in a very short time, given sufficient levels of interaction between members of a group.

Language evolution and human history: what a difference a date makes

Historical inference is at its most powerful when independent lines of evidence can be integrated into a coherent account. Dating linguistic and cultural lineages can potentially play a vital role in the integration of evidence from linguistics, anthropology, archaeology and genetics. Unfortunately, although the comparative method in historical linguistics can provide a relative chronology, it cannot provide absolute date estimates and an alternative approach, called glottochronology, is fundamentally flawed. In this paper we outline how computational phylogenetic methods can reliably estimate language divergence dates and thus help resolve long-standing debates about human prehistory ranging from the origin of the Indo-European language family to the peopling of the Pacific.

Phonetic comparison, varieties, and networks

While his eponymous basic vocabulary lists and the study of language divergence may be Swadesh’s most appreciated legacies, we demonstrate that phonetic quantification of language varieties also follows very much in the tradition of Swadesh’s own work. We compare a few different measures of phonetic distance on a very small set of data from Germanic varieties, showing the influence of lexicostatistics and the relevance of Swadesh’s ‘Mesh Principle’. What we emphasise overall is that Swadesh’s influence is palpable, even in domains outside those for which he is best remembered.