Multiplex lexical networks reveal patterns in early word acquisition in children

Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N = 529 words/nodes are connected according to four relationship: (i) free association, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We investigate the topology of the resulting multiplex and then proceed to evaluate single layers and the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the multiplex topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex structure is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition

Holistic processing of hierarchical structures in connectionist networks

Despite the success of connectionist systems to model some aspects of cognition, critics argue that the lack of symbol processing makes them inadequate for modelling high-level cognitive tasks which require the representation and processing of hierarchical structures. In this thesis we investigate four mechanisms for encoding hierarchical structures in distributed representations that are suitable for processing in connectionist systems: Tensor Product Representation, Recursive Auto-Associative Memory (RAAM), Holographic Reduced Representation (HRR), and Binary Spatter Code (BSC). In these four schemes representations of hierarchical structures are either learned in a connectionist network or constructed by means of various mathematical operations from binary or real-value vectors.It is argued that the resulting representations carry structural information without being themselves syntactically structured. The structural information about a represented object is encoded in the position of its representation in a high-dimensional representational space. We use Principal Component Analysis and constructivist networks to show that well-separated clusters consisting of representations for structurally similar hierarchical objects are formed in the representational spaces of RAAMs and HRRs. The spatial structure of HRRs and RAAM representations supports the holistic yet structure-sensitive processing of them. Holistic operations on RAAM representations can be learned by backpropagation networks. However, holistic operators over HRRs, Tensor Products, and BSCs have to be constructed by hand, which is not a desirable situation. We propose two new algorithms for learning holistic transformations of HRRs from examples. These algorithms are able to generalise the acquired knowledge to hierarchical objects of higher complexity than the training examples. Such generalisations exhibit systematicity of a degree which, to our best knowledge, has not yet been achieved by any other comparable learning method.Finally, we outline how a number of holistic transformations can be learned in parallel and applied to representations of structurally different objects. The ability to distinguish and perform a number of different structure-sensitive operations is one step towards a connectionist architecture that is capable of modelling complex high-level cognitive tasks such as natural language processing and logical inference

Sentiment classification using tree‐based gated recurrent units

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing is one of the most challenging fields of Artificial Intelligence. The past 10 years, this field has witnessed a fascinating progress due to Deep Learning. Despite that, we haven’t achieved to build an architecture of models that can understand natural language as humans do. Many architectures have been proposed, each of them having its own strengths and weaknesses. In this report, we will cover the tree based architectures and in particular we will propose a different tree based architecture that is very similar to the Tree-Based LSTM, proposed by Tai(2015). In this work, we aim to make a critical comparison between the proposed architecture -Tree-Based GRU- with Tree-based LSTM for sentiment classification tasks, both binary and fine-grained

ATMS-Based architecture for stylistics-aware text generation

This thesis is concerned with the effect of surface stylistic constraints (SSC) on syntactic and lexical choice within a unified generation architecture. Despite the fact that these issues have been investigated by researchers in the field, little work has been done with regard to system architectures that allow surface form constraints to influence earlier linguistic or even semantic decisions made throughout the NLG process. By SSC we mean those stylistic requirements that are known beforehand but cannot be tested until after the utterance or — in some lucky cases — until a proper linearised part of it has been generated. These include collocational constraints, text size limits, and poetic aspects such as rhyme and metre to name a few. This thesis introduces a new NLG architecture that can be sensitive to surface stylistic requirements. It brings together a well-founded linguistic theory that has been used in many successful NLG systems (Systemic Functional Linguistics, SFL) and an exist¬ ing AI search mechanism (the Assumption-based Truth Maintenance System, ATMS) which caches important search information and avoids work duplication. To this end, the thesis explores the logical relation between the grammar formalism and the search technique. It designs, based on that logical connection, an algorithm for the automatic translation of systemic grammar networks to ATMS dependency networks. The generator then uses the translated networks to generate natural language texts with a high paraphrasing power as a direct result of its ability to pursue multiple paths simultaneously. The thesis approaches the crucial notion of choice differently to previ¬ ous systems using SFL. It relaxes the choice process in that choosers are not obliged to deterministically choose a single alternative allowing SSC to influence the final lexical and syntactic decisions. The thesis also develops a situation-action framework for the specification of stylistic requirements independently of the micro-semantic input. The user or application can state what surface requirements they wish to impose and the ATMS-based generator then attempts to satisfy these constraints. Finally, a prototype ATMS-based generation system embodying the ideas presented in this thesis is implemented and evaluated. We examine the system's stylistic sensitivity by testing it on three different sets of stylistic requirements, namely: collocational, size, and poetic constraints

An investigation of the electrolytic plasma oxidation process for corrosion protection of pure magnesium and magnesium alloy AM50.

In this study, silicate and phosphate EPO coatings were produced on pure magnesium using an AC power source. It was found that the silicate coatings possess good wear resistance, while the phosphate coatings provide better corrosion protection. A Design of Experiment (DOE) technique, the Taguchi method, was used to systematically investigate the effect of the EPO process parameters on the corrosion protection properties of a coated magnesium alloy AM50 using a DC power. The experimental design consisted of four factors (treatment time, current density, and KOH and NaAlO2 concentrations), with three levels of each factor. Potentiodynamic polarization measurements were conducted to determine the corrosion resistance of the coated samples. The optimized processing parameters are 12 minutes, 12 mA/cm2 current density, 0.9 g/l KOH, 15.0 g/l NaAlO2. The results of the percentage contribution of each factor determined by the analysis of variance (ANOVA) imply that the KOH concentration is the most significant factor affecting the corrosion resistance of the coatings, while treatment time is a major factor affecting the thickness of the coatings. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M323. Source: Masters Abstracts International, Volume: 44-03, page: 1479. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Neural Networks forBuilding Semantic Models and Knowledge Graphs

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInoopenFutia, Giusepp

An investigation of grammar design in natural-language speech-recognition.

With the growing interest and demand for human-machine interaction, much work concerning speech-recognition has been carried out over the past three decades. Although a variety of approaches have been proposed to address speech-recognition issues, such as stochastic (statistical) techniques, grammar-based techniques, techniques integrated with linguistic features, and other approaches, recognition accuracy and robustness remain among the major problems that need to be addressed. At the state of the art, most commercial speech products are constructed using grammar-based speech-recognition technology. In this thesis, we investigate a number of features involved in grammar design in natural-language speech-recognition technology. We hypothesize that: with the same domain, a semantic grammar, which directly encodes some semantic constraints into the recognition grammar, achieves better accuracy, but less robustness; a syntactic grammar defines a language with a larger size, thereby it has better robustness, but less accuracy; a word-sequence grammar, which includes neither semantics nor syntax, defines the largest language, therefore, is the most robust, but has very poor recognition accuracy. In this Master\u27s thesis, we claim that proper grammar design can achieve the appropriate compromise between recognition accuracy and robustness. The thesis has been proven by experiments using the IBM Voice-Server SDK, which consists of a VoiceXML browser, IBM ViaVoice Speech Recognition and Text-To-Speech (TTS) engines, sample applications, and other tools for developing and testing VoiceXML applications. The experimental grammars are written in the Java Speech Grammar Format (JSGF), and the testing applications are written in VoiceXML. The tentative experimental results suggest that grammar design is a good area for further study. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .S555. Source: Masters Abstracts International, Volume: 43-01, page: 0244. Adviser: Richard A. Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Making suggestions at business meetings

In problem solving and decision making discussions, proposals and suggestions are crucial elements of the interaction. In analysis it is not a straightforward task to identify the acts of 'suggesting'. Traditional speech act typologies are inadequate because they tend to assume that categorical boundaries exist between different act types. In this thesis, therefore, I first establish a method of identifying SUGGESTIONS. I suggest that we use a system network in which different copattemings of paradigmatic choices constitute different types of directive acts. From the potential choices in the network, SUGGESTIONS are defined as acts in which the speaker proposes a future action which is optional and presented as beneficial or desirable to the addressee, the group, or the company in general (often all three at the same time). Next, I investigate how these elements, in particular the evaluative meanings of benefit and desirability, are marked linguistically. The indicators are primarily lexical while some coincide with modal expressions indicating other modal meanings (e.g. necessity, obligation, ability, etc.). The modal meanings of benefit/desirability and other modal meanings conflate, modifying the latter in the process. Meanings of benefit and desirability in lexical choices are generally only recoverable through reference to textual context (i.e. what previous speakers have said about the topic in question) and the situational context of the speech event (i.e. business meetings and relevant values). Status and tact influence the constellation of modal meanings. The values, roles and expectations linked to the speech event also explain the structure and shape of the chain of suggestions. Studies of other types of speech events have revealed common structural patterns (e.g. preferred responses to specific acts). The freest parts of meetings (i.e. not the opening, closing, or reporting sessions) are however characterised by a lack of such structure. Surprisingly often a SUGGESTION is not met by a direct evaluation of the SUGGESTION but just with another speaker's SUGGESTION. It turns out that what structures the discussions, instead, are values recoverable from the textual context which is itself anchored in the situational context. In other words, evaluative meanings of benefit/desirability, which are formulated by speakers, are based on values from the business culture. These values link up the contributions made by speakers across the entire meeting (or series of meetings) and create coherence. Interpersonal meaning is thus involved in coherence and text building; a textual function is derived from the interpersonal function