NLPinAI 2018 Abstracts

Full Papers
Full Papers
Paper Nr: 3
Title:

Supervised Classification of Aspectual Verb Classes in German - Subcategorization-Frame-Based vs Window-Based Approach: A Comparison
Authors:

Jürgen Hermes, Michael Richter and Claes Neuefeind
Abstract: The present study examines the results of two experiments: the aspectual classification of German verbs within a window-based distributional framework and the classification within a subcategorization-frame-based framework. The predictive power of pure, unstructured co-occurrences of verbs is compared against that of linguistically motivated, well defined co-occurrences which we denote as informed distributional framework. Using a support vector machine classifier and a classification into an extended Vendler classification (Vendler, 1967) as the gold standard, we observe excellent results in both frameworks which perform almost on a par.
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Paper Nr: 6
Title:

Lexical and Morpho-syntactic Features in Word Embeddings - A Case Study of Nouns in Swedish
Authors:

Ali Basirat and Marc Tang
Abstract: We apply real-valued word vectors combined with two different types of classifiers (linear discriminant analy- sis and feed-forward neural network) to scrutinize whether basic nominal categories can be captured by simple word embedding models. We also provide a linguistic analysis of the errors generated by the classifiers. The targeted language is Swedish, in which we investigate three nominal aspects: uter/neuter, common/proper, and count/mass. They represent respectively grammatical, semantic, and mixed types of nominal classification within languages. Our results show that word embeddings can capture typical grammatical and semantic fea- tures such as uter/neuter and common/proper nouns. Nevertheless, the model encounters difficulties to identify classes such as count/mass which not only combine both grammatical and semantic properties, but are also subject to conversion and shift. Hence, we answer the call of the Special Session on Natural Language Process- ing in Artificial Intelligence by approaching the topic of interfaces between morphology, lexicon, semantics, and syntax via interdisciplinary methods combining machine learning of language and general linguistics.
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Paper Nr: 8
Title:

A Distributional Semantics Model for Idiom Detection - The Case of English and Russian
Authors:

Jing Peng, Katsiaryna Aharodnik and Anna Feldman
Abstract: This paper describes experiments in English and Russian automatic idiom detection. Our algorithm is based on the idea that literal and idiomatic expressions appear in different contexts. This difference is captured by our distributional semantics model. We evaluate our model on both languages and compare its results. We show that our model is language-independent. We also describe a new annotated resource we created for our experiments.
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Paper Nr: 9
Title:

Predicting Cognitive Impairments with a Mobile Application
Authors:

Elif Eyigöz, Guillermo Cecchi and Ravi Tejwani
Abstract: Assessment of cognitive impairments is of social and clinical importance for vulnerable populations, such as elderly, athletes and soldiers, who are prone to falling victim to cognitive impairments. This paper presents ongoing work for developing an application that predicts the neurological state of users with the state-of-the art performance through analyzing the structural complexity of users' utterances. We present a novel method that estimates the neurological state of users with Pearson correlation of 0.66 with respect to the Mini-mental state exam score. Unlike previous work, our method does not depend on assumptions of relating linguistics representations to human language-processing capabilities, but discovers the discriminative patterns automatically.
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