Elias Iosif


2018

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Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations
Nikos Athanasiou | Elias Iosif | Alexandros Potamianos
Proceedings of the 27th International Conference on Computational Linguistics

Neural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word semantics and localized neural activations. Successful mappings let us expand to the full lexicon of concrete nouns using the assumption that similarity of meaning implies similar neural activation patterns. In this paper, we propose a computational model that estimates semantic similarity in the neural activation space and investigates the relative performance of this model for various natural language processing tasks. Despite the simplicity of the proposed model and the very small number of example words used to bootstrap it, the neural activation semantic model performs surprisingly well compared to state-of-the-art word embeddings. Specifically, the neural activation semantic model performs better than the state-of-the-art for the task of semantic similarity estimation between very similar or very dissimilar words, while performing well on other tasks such as entailment and word categorization. These are strong indications that neural activation semantic models can not only shed some light into human cognition but also contribute to computation models for certain tasks.

2017

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Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
Athanasia Kolovou | Filippos Kokkinos | Aris Fergadis | Pinelopi Papalampidi | Elias Iosif | Nikolaos Malandrakis | Elisavet Palogiannidi | Haris Papageorgiou | Shrikanth Narayanan | Alexandros Potamianos
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter. Specifically the proposed system participated both to tweet polarity classification (two-, three- and five class) and tweet quantification (two and five-class) tasks.

2016

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The SpeDial datasets: datasets for Spoken Dialogue Systems analytics
José Lopes | Arodami Chorianopoulou | Elisavet Palogiannidi | Helena Moniz | Alberto Abad | Katerina Louka | Elias Iosif | Alexandros Potamianos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of cycle of development of new Spoken Dialogue Systems (SDSs). The datasets include audios and several manual annotations, i.e., miscommunication, anger, satisfaction, repetition, gender and task success. The datasets were created with data from real users and cover two different languages: English and Greek. Detectors for miscommunication, anger and gender were trained for both systems. The detectors were particularly accurate in tasks where humans have high annotator agreement such as miscommunication and gender. As expected due to the subjectivity of the task, the anger detector had a less satisfactory performance. Nevertheless, we proved that the automatic detection of situations that can lead to problems in SDSs is possible and can be a promising direction to reduce the duration of SDS’s development cycle.

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Cognitively Motivated Distributional Representations of Meaning
Elias Iosif | Spiros Georgiladakis | Alexandros Potamianos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Although meaning is at the core of human cognition, state-of-the-art distributional semantic models (DSMs) are often agnostic to the findings in the area of semantic cognition. In this work, we present a novel type of DSMs motivated by the dual-processing cognitive perspective that is triggered by lexico-semantic activations in the short-term human memory. The proposed model is shown to perform better than state-of-the-art models for computing semantic similarity between words. The fusion of different types of DSMs is also investigated achieving results that are comparable or better than the state-of-the-art. The used corpora along with a set of tools, as well as large repositories of vectorial word representations are made publicly available for four languages (English, German, Italian, and Greek).

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Affective Lexicon Creation for the Greek Language
Elisavet Palogiannidi | Polychronis Koutsakis | Elias Iosif | Alexandros Potamianos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Starting from the English affective lexicon ANEW (Bradley and Lang, 1999a) we have created the first Greek affective lexicon. It contains human ratings for the three continuous affective dimensions of valence, arousal and dominance for 1034 words. The Greek affective lexicon is compared with affective lexica in English, Spanish and Portuguese. The lexicon is automatically expanded by selecting a small number of manually annotated words to bootstrap the process of estimating affective ratings of unknown words. We experimented with the parameters of the semantic-affective model in order to investigate their impact to its performance, which reaches 85% binary classification accuracy (positive vs. negative ratings). We share the Greek affective lexicon that consists of 1034 words and the automatically expanded Greek affective lexicon that contains 407K words.

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Crossmodal Network-Based Distributional Semantic Models
Elias Iosif | Alexandros Potamianos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Despite the recent success of distributional semantic models (DSMs) in various semantic tasks they remain disconnected with real-world perceptual cues since they typically rely on linguistic features. Text data constitute the dominant source of features for the majority of such models, although there is evidence from cognitive science that cues from other modalities contribute to the acquisition and representation of semantic knowledge. In this work, we propose the crossmodal extension of a two-tier text-based model, where semantic representations are encoded in the first layer, while the second layer is used for computing similarity between words. We exploit text- and image-derived features for performing computations at each layer, as well as various approaches for their crossmodal fusion. It is shown that the crossmodal model performs better (from 0.68 to 0.71 correlation coefficient) than the unimodal one for the task of similarity computation between words.

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A semantic-affective compositional approach for the affective labelling of adjective-noun and noun-noun pairs
Elisavet Palogiannidi | Elias Iosif | Polychronis Koutsakis | Alexandros Potamianos
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation
Elisavet Palogiannidi | Athanasia Kolovou | Fenia Christopoulou | Filippos Kokkinos | Elias Iosif | Nikolaos Malandrakis | Haris Papageorgiou | Shrikanth Narayanan | Alexandros Potamianos
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Feeling is Understanding: From Affective to Semantic Spaces
Elias Iosif | Alexandros Potamianos
Proceedings of the 11th International Conference on Computational Semantics

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Fusion of Compositional Network-based and Lexical Function Distributional Semantic Models
Spiros Georgiladakis | Elias Iosif | Alexandros Potamianos
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics

2014

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SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue Systems
Ioannis Klasinas | Elias Iosif | Katerina Louka | Alexandros Potamianos
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection
Arodami Chorianopoulou | Georgia Athanasopoulou | Elias Iosif | Ioannis Klasinas | Alexandros Potamianos
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Word Semantic Similarity for Morphologically Rich Languages
Kalliopi Zervanou | Elias Iosif | Alexandros Potamianos
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this work, we investigate the role of morphology on the performance of semantic similarity for morphologically rich languages, such as German and Greek. The challenge in processing languages with richer morphology than English, lies in reducing estimation error while addressing the semantic distortion introduced by a stemmer or a lemmatiser. For this purpose, we propose a methodology for selective stemming, based on a semantic distortion metric. The proposed algorithm is tested on the task of similarity estimation between words using two types of corpus-based similarity metrics: co-occurrence-based and context-based. The performance on morphologically rich languages is boosted by stemming with the context-based metric, unlike English, where the best results are obtained by the co-occurrence-based metric. A key finding is that the estimation error reduction is different when a word is used as a feature, rather than when it is used as a target word.

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Low-Dimensional Manifold Distributional Semantic Models
Georgia Athanasopoulou | Elias Iosif | Alexandros Potamianos
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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From Speaker Identification to Affective Analysis: A Multi-Step System for Analyzing Children’s Stories
Elias Iosif | Taniya Mishra
Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL)

2013

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Semantic Similarity Computation for Abstract and Concrete Nouns Using Network-based Distributional Semantic Models
Elias Iosif | Alexandros Potamianos | Maria Giannoudaki | Kalliopi Zervanou
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Short Papers

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DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation
Nikolaos Malandrakis | Elias Iosif | Vassiliki Prokopi | Alexandros Potamianos | Shrikanth Narayanan
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks
Elias Iosif | Alexandros Potamianos
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We investigate the creation of corpora from web-harvested data following a scalable approach that has linear query complexity. Individual web queries are posed for a lexicon that includes thousands of nouns and the retrieved data are aggregated. A lexical network is constructed, in which the lexicon nouns are linked according to their context-based similarity. We introduce the notion of semantic neighborhoods, which are exploited for the computation of semantic similarity. Two types of normalization are proposed and evaluated on the semantic tasks of: (i) similarity judgement, and (ii) noun categorization and taxonomy creation. The created corpus along with a set of tools and noun similarities are made publicly available.

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Associative and Semantic Features Extracted From Web-Harvested Corpora
Elias Iosif | Maria Giannoudaki | Eric Fosler-Lussier | Alexandros Potamianos
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We address the problem of automatic classification of associative and semantic relations between words, and particularly those that hold between nouns. Lexical relations such as synonymy, hypernymy/hyponymy, constitute the fundamental types of semantic relations. Associative relations are harder to define, since they include a long list of diverse relations, e.g., """"Cause-Effect"""", """"Instrument-Agency"""". Motivated by findings from the literature of psycholinguistics and corpus linguistics, we propose features that take advantage of general linguistic properties. For evaluation we merged three datasets assembled and validated by cognitive scientists. A proposed priming coefficient that measures the degree of asymmetry in the order of appearance of the words in text achieves the best classification results, followed by context-based similarity metrics. The web-based features achieve classification accuracy that exceeds 85%.

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DeepPurple: Estimating Sentence Semantic Similarity using N-gram Regression Models and Web Snippets
Nikos Malandrakis | Elias Iosif | Alexandros Potamianos
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)