A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet

Frame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision

An enhanced sequential exception technique for semantic-based text anomaly detection

The detection of semantic-based text anomaly is an interesting research area which has gained considerable attention from the data mining community. Text anomaly detection identifies deviating information from general information contained in documents. Text data are characterized by having problems related to ambiguity, high dimensionality, sparsity and text representation. If these challenges are not properly resolved, identifying semantic-based text anomaly will be less accurate. This study proposes an Enhanced Sequential Exception Technique (ESET) to detect semantic-based text anomaly by achieving five objectives: (1) to modify Sequential Exception Technique (SET) in processing unstructured text; (2) to optimize Cosine Similarity for identifying similar and dissimilar text data; (3) to hybridize modified SET with Latent Semantic Analysis (LSA); (4) to integrate Lesk and Selectional Preference algorithms for disambiguating senses and identifying text canonical form; and (5) to represent semantic-based text anomaly using First Order Logic (FOL) and Concept Network Graph (CNG). ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. Then, FOL and CNG are proposed to represent the detected semantic-based text anomaly. To demonstrate the feasibility of the technique, four selected datasets namely NIPS data, ENRON, Daily Koss blog, and 20Newsgroups were experimented on. The experimental evaluation revealed that ESET has significantly improved the accuracy of detecting semantic-based text anomaly from documents. When compared with existing measures, the experimental results outperformed benchmarked methods with an improved F1-score from all datasets respectively; NIPS data 0.75, ENRON 0.82, Daily Koss blog 0.93 and 20Newsgroups 0.97. The results generated from ESET has proven to be significant and supported a growing notion of semantic-based text anomaly which is increasingly evident in existing literatures. Practically, this study contributes to topic modelling and concept coherence for the purpose of visualizing information, knowledge sharing and optimized decision making

Automatic Image Captioning with Style

This thesis connects two core topics in machine learning, vision and language. The problem of choice is image caption generation: automatically constructing natural language descriptions of image content. Previous research into image caption generation has focused on generating purely descriptive captions; I focus on generating visually relevant captions with a distinct linguistic style. Captions with style have the potential to ease communication and add a new layer of personalisation. First, I consider naming variations in image captions, and propose a method for predicting context-dependent names that takes into account visual and linguistic information. This method makes use of a large-scale image caption dataset, which I also use to explore naming conventions and report naming conventions for hundreds of animal classes. Next I propose the SentiCap model, which relies on recent advances in artificial neural networks to generate visually relevant image captions with positive or negative sentiment. To balance descriptiveness and sentiment, the SentiCap model dynamically switches between two recurrent neural networks, one tuned for descriptive words and one for sentiment words. As the first published model for generating captions with sentiment, SentiCap has influenced a number of subsequent works. I then investigate the sub-task of modelling styled sentences without images. The specific task chosen is sentence simplification: rewriting news article sentences to make them easier to understand. For this task I design a neural sequence-to-sequence model that can work with limited training data, using novel adaptations for word copying and sharing word embeddings. Finally, I present SemStyle, a system for generating visually relevant image captions in the style of an arbitrary text corpus. A shared term space allows a neural network for vision and content planning to communicate with a network for styled language generation. SemStyle achieves competitive results in human and automatic evaluations of descriptiveness and style. As a whole, this thesis presents two complete systems for styled caption generation that are first of their kind and demonstrate, for the first time, that automatic style transfer for image captions is achievable. Contributions also include novel ideas for object naming and sentence simplification. This thesis opens up inquiries into highly personalised image captions; large scale visually grounded concept naming; and more generally, styled text generation with content control

Meaning in Distributions : A Study on Computational Methods in Lexical Semantics

This study investigates the connection between lexical items' distributions and their meanings from the perspective of computational distributional operations. When applying computational methods in meaning-related research, it is customary to refer to the so-called distributional hypothesis, according to which differences in distributions and meanings are mutually correlated. However, making use of such a hypothesis requires critical explication of the concept of distribution and plausible arguments for why any particular distributional structure is connected to a particular meaning-related phenomenon. In broad strokes, the present study seeks to chart the major differences in how the concept of distribution is conceived in structuralist/autonomous and usage-based/functionalist theoretical families of contemporary linguistics. The two theoretical positions on distributions are studied for identifying how meanings could enter as enabling or constraining factors in them. The empirical part of the study comprises two case studies. In the first one, three pairs of antonymical adjectives (köyhä/rikas, sairas/terve and vanha/nuori) are studied distributionally. Very narrow bag-of-word vector representations of distributions show how the dimensions on which relevant distributional similarities are based already conflate unexpected and varied range of linguistic phenomena, spanning from syntax-oriented conceptual constrainment to connotations, pragmatic patterns and affectivity. Thus, the results simultaneously corroborate the distributional hypothesis and challenge its over-generalized, uncritical applicability. For the study of meaning, distributional and semantic spaces cannot be treated as analogous by default. In the second case study, a distributional operation is purposefully built for answering a research question related to historical development of Finnish social law terminology in the period of 1860–1910. Using a method based on interlinked collocation networks, the study shows how the term vaivainen (‘pauper, beggar, measly’) receded from the prestigious legal and administrative registers during the studied period. Corroborating some of the findings of the previous parts of this dissertation, the case study shows how structures found in distributional representations cannot be satisfactorily explained without relying on semantic, pragmatic and discoursal interpretations. The analysis leads to confirming the timeline of the studied word use in the given register. It also shows how the distributional methods based on networked patterns of co-occurrence highlight incomparable structures of very different nature and skew towards frequent occurrence types prevalent in the data.Nykyaikaiset laskennalliset menetelmät suorittavat suurista tekstiaineistoista koottujen tilastollisten mallien avulla lähes virheettömästi monia sanojen merkitysten ymmärtämistä edellyttäviä tehtäviä. Kielitieteellisen metodologian kannalta onkin kiinnostavaa, miten tällaiset menetelmät sopivat kiellisten rakenteiden merkitysten lingvistiseen tutkimukseen. Tämä väitöstutkimus lähestyy kysymystä sanasemantiikan näkökulmasta ja pyrkii sekä teoreettisesti että empiirisesti kuvaamaan minkälaisia merkityksen lajeja pelkkiin sanojen sekvensseihin perustuvat laskennalliset menetelmät kykenevät tavoittamaan. Väitöstutkimus koostuu kahdesta osatutkimuksesta, joista ensimmäisessä tutkitaan kolmea vastakohtaista adjektiiviparia Suomi24-aineistosta kootun vektoriavaruusmallin avulla. Tulokset osoittavat, miten jo hyvin rajatut sekvenssiympäristöt sisältävät informaatiota käsitteellisten merkitysten lisäksi myös muun muassa niiden konnotaatioista ja affektiivisuudesta. Sekvenssiympäristön tuottama kuva merkityksestä on kuitenkin kattavuudeltaan ennalta-arvaamaton ja ne kielekäyttötavat, jotka tutkimusaineistossa ovat yleisiä vaikuttavat selvästi siihen mitä merkityksen piirteitä tulee näkyviin. Toisessa osatutkimuksessa jäljitetään erään sosiaalioikeudellisen termin, vaivaisen, historiaa 1800-luvun loppupuolella Kansalliskirjaston historiallisesta digitaalisesta sanomalehtikokoelmasta. Myötäesiintymäverkostojen avulla pyritään selvittämään miten se katosi oikeuskielestä tunnistamalla aineistosta hallinnollis-juridista rekisteriä vastaava rakenne ja seuraamalla vaivaisen asemaa siinä. Menetelmänä käytetyt myötäesiintymäverkostot eivät kuitenkaan edusta puhtaasti mitään tiettyä rekisteriä, vaan sekoittavat itseensä piirteitä erilaisista kategorioista, joilla kielen käyttöä on esimerkiksi tekstintutkimuksessa kuvattu. Tiheimmät verkostot muodostuvat rekisterien, genrejen, tekstityyppien ja sanastollisen koheesion yhteisvaikutuksesta. Osatutkimuksen tulokset antavat viitteitä siitä, että tämä on yleinen piirre monissa samankaltaisissa menetelmissä, mukaan lukien yleiset aihemallit