Automatic detection of cognates helps downstream NLP tasks of Machine
Translation, Cross-lingual Information Retrieval, Computational Phylogenetics
and Cross-lingual Named Entity Recognition. Previous approaches for the task of
cognate detection use orthographic, phonetic and semantic similarity based
features sets. In this paper, we propose a novel method for enriching the
feature sets, with cognitive features extracted from human readers' gaze
behaviour. We collect gaze behaviour data for a small sample of cognates and
show that extracted cognitive features help the task of cognate detection.
However, gaze data collection and annotation is a costly task. We use the
collected gaze behaviour data to predict cognitive features for a larger sample
and show that predicted cognitive features, also, significantly improve the
task performance. We report improvements of 10% with the collected gaze
features, and 12% using the predicted gaze features, over the previously
proposed approaches. Furthermore, we release the collected gaze behaviour data
along with our code and cross-lingual models.

本文提出了一种新的方法，利用人类阅读者的凝视行为提取认知特征，用于自动检测同源词，并通过使用搜集到的和预测的视线行为数据，证明该方法可以帮助提高同源词检测任务的性能提高 10%，相比之前的方法预测视线行为数据的性能提高了 12%。

认知感知同源词检测

Cognition-aware Cognate Detection

In this paper, we propose a novel mechanism for enriching the feature vector,
for the task of sarcasm detection, with cognitive features extracted from
eye-movement patterns of human readers. Sarcasm detection has been a
challenging research problem, and its importance for NLP applications such as
review summarization, dialog systems and sentiment analysis is well recognized.
Sarcasm can often be traced to incongruity that becomes apparent as the full
sentence unfolds. This presence of incongruity- implicit or explicit- affects
the way readers eyes move through the text. We observe the difference in the
behaviour of the eye, while reading sarcastic and non sarcastic sentences.
Motivated by his observation, we augment traditional linguistic and stylistic
features for sarcasm detection with the cognitive features obtained from
readers eye movement data. We perform statistical classification using the
enhanced feature set so obtained. The augmented cognitive features improve
sarcasm detection by 3.7% (in terms of F-score), over the performance of the
best reported system.

介绍了一种从阅读者的眼动模式中提取认知特征，用于增强特征向量，以提高讽刺检测的性能的新机制。使用增强的特征集进行统计分类，性能提高了 3.7％（以 F-score 为度量），超过了最佳报告系统的性能。

利用认知特征进行讽刺检测

Harnessing Cognitive Features for Sarcasm Detection

Sentiments expressed in user-generated short text and sentences are nuanced
by subtleties at lexical, syntactic, semantic and pragmatic levels. To address
this, we propose to augment traditional features used for sentiment analysis
and sarcasm detection, with cognitive features derived from the eye-movement
patterns of readers. Statistical classification using our enhanced feature set
improves the performance (F-score) of polarity detection by a maximum of 3.7%
and 9.3% on two datasets, over the systems that use only traditional features.
We perform feature significance analysis, and experiment on a held-out dataset,
showing that cognitive features indeed empower sentiment analyzers to handle
complex constructs.

本研究提出一种使用读者眼动模式获取认知特征的方法，以增强情感分析和讽刺检测的性能，结果表明：使用这种增强型特征集合可以将极性检测的 F 分数最高提升 3.7％和 9.3％。