Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category

Extrapolating Subjectivity Research to Other Languages

Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the implementation of techniques on par with those developed for English, and by applying methods that are lighter in the usage of text processing infrastructure, we are able to conduct multilingual subjectivity research in these languages as well. Since my aim is also to minimize the amount of manual work required to develop lexica or corpora in these languages, the techniques proposed employ a lever approach, where English often acts as the donor language (the fulcrum in a lever) and allows through a relatively minimal amount of effort to establish preliminary subjectivity research in a target language

Exploiting Wikipedia Semantics for Computing Word Associations

Semantic association computation is the process of automatically quantifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to computing semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the implicit semantic relations. In the past few years, semantic association computation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of humans' associations than the approaches based on other structured and unstructured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it effectively exploited the knowledge source aspect of Wikipedia by developing new measures of semantic associations based on Wikipedia hyperlink structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for computing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based features performs better than those based on individual features. It was also found that the structure based measures outperformed the informative content based measures on both semantic similarity and semantic relatedness computation tasks. The second contribution of the research work in the thesis is that it effectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based measure using the idea of directional context inspired by the free word association task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based measures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and discussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans' estimates of word associations

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Computational foundations of phenomenology

The purpose of the dissertation is to investigate the degree of compatibility of two fields: phenomenology and computational cognitive science. The former field proposes to explicate all structures of conscious experience in terms of conscious experience. The latter proposes to explicate all structures of consciousness partly in terms of unconscious causal factors. These endeavors have been seen as mutually exclusive. I put forward the thesis that the original formulation of phenomenology may be seen to have a computational theory of mind in the background. To this end, I show in the first chapter that the founder of phenomenology articulated, prior to founding phenomenology, a computational theory of mind in terms of its two modern theses: (1) syntactic representations, and (2) their causal generation and interaction. Insofar as I am able to provide sufficient evidence for this thesis, I am theoretically licensed to proceed to trace its influence on the founding of phenomenology proper. On the above textual basis, I proceed in the second chapter to discuss Husserl's methodology in the founding work of phenomenology - the Logical Investigations. I there show how my compatibility thesis may be true; indeed, I demonstrate that formal evidence is the causal product of what Husserl calls “unsere Denkmaschine” – a thought-machine that manipulates syntactic symbols. The third chapter discusses several arguments against (Humean) associationism, and by extension against (Churchlandian) connectionism, and show that they demand in their stead computationalism, both on account of the nature of the explananda as well as for the sake of theoretical completeness. In the fourth chapter, I discuss, with a view to deepening my interpretation, the much-celebrated property (since Chomsky) of productivity. This leads to a discussion of the methodological relation between “universal grammar,” as it appears directly in the 4th Logical Investigation, and the computational theory of mind. In the fifth chapter, I discuss how Husserl’s descriptive treatment of the propositional attitudes (as act-matters & act-qualities), nominalization, and categorial intuition may be supplemented on the explanatory side by a language of thought

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

An integrated semantic-based framework for intelligent similarity measurement and clustering of microblogging posts

Twitter, the most popular microblogging platform, is gaining rapid prominence as a source of information sharing and social awareness due to its popularity and massive user generated content. These include applications such as tailoring advertisement campaigns, event detection, trends analysis, and prediction of micro-populations. The aforementioned applications are generally conducted through cluster analysis of tweets to generate a more concise and organized representation of the massive raw tweets. However, current approaches perform traditional cluster analysis using conventional proximity measures, such as Euclidean distance. However, the sheer volume, noise, and dynamism of Twitter, impose challenges that hinder the efficacy of traditional clustering algorithms in detecting meaningful clusters within microblogging posts. The research presented in this thesis sets out to design and develop a novel short text semantic similarity (STSS) measure, named TREASURE, which captures the semantic and structural features of microblogging posts for intelligently predicting the similarities. TREASURE is utilised in the development of an innovative semantic-based cluster analysis algorithm (SBCA) that contributes in generating more accurate and meaningful granularities within microblogging posts. The integrated semantic-based framework incorporating TREASURE and the SBCA algorithm tackles both the problem of microblogging cluster analysis and contributes to the success of a variety of natural language processing (NLP) and computational intelligence research. TREASURE utilises word embedding neural network (NN) models to capture the semantic relationships between words based on their co-occurrences in a corpus. Moreover, TREASURE analyses the morphological and lexical structure of tweets to predict the syntactic similarities. An intrinsic evaluation of TREASURE was performed with reference to a reliable similarity benchmark generated through an experiment to gather human ratings on a Twitter political dataset. A further evaluation was performed with reference to the SemEval-2014 similarity benchmark in order to validate the generalizability of TREASURE. The intrinsic evaluation and statistical analysis demonstrated a strong positive linear correlation between TREASURE and human ratings for both benchmarks. Furthermore, TREASURE achieved a significantly higher correlation coefficient compared to existing state-of-the-art STSS measures. The SBCA algorithm incorporates TREASURE as the proximity measure. Unlike conventional partition-based clustering algorithms, the SBCA algorithm is fully unsupervised and dynamically determine the number of clusters beforehand. Subjective evaluation criteria were employed to evaluate the SBCA algorithm with reference to the SemEval-2014 similarity benchmark. Furthermore, an experiment was conducted to produce a reliable multi-class benchmark on the European Referendum political domain, which was also utilised to evaluate the SBCA algorithm. The evaluation results provide evidence that the SBCA algorithm undertakes highly accurate combining and separation decisions and can generate pure clusters from microblogging posts. The contributions of this thesis to knowledge are mainly demonstrated as: 1) Development of a novel STSS measure for microblogging posts (TREASURE). 2) Development of a new SBCA algorithm that incorporates TREASURE to detect semantic themes in microblogs. 3) Generating a word embedding pre-trained model learned from a large corpus of political tweets. 4) Production of a reliable similarity-annotated benchmark and a reliable multi-class benchmark in the domain of politics

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.European research projects - CoSpaces (IST-5-034245), CRESCENDO (FP7-234344) and MobiS (FP7-318452