The Case for the Journal’s Use of a CC-BY License

Journal of Language Modelling provides its articles under a Creative Commons CC-BY license. We discuss why this is the appropriate choice for the journal.Engineering and Applied Science

A Proof-Theoretic Approach to Scope Ambiguity in Compositional Vector Space Models

We investigate the extent to which compositional vector space models can be used to account for scope ambiguity in quantified sentences (of the form "Every man loves some woman"). Such sentences containing two quantifiers introduce two readings, a direct scope reading and an inverse scope reading. This ambiguity has been treated in a vector space model using bialgebras by (Hedges and Sadrzadeh, 2016) and (Sadrzadeh, 2016), though without an explanation of the mechanism by which the ambiguity arises. We combine a polarised focussed sequent calculus for the non-associative Lambek calculus NL, as described in (Moortgat and Moot, 2011), with the vector based approach to quantifier scope ambiguity. In particular, we establish a procedure for obtaining a vector space model for quantifier scope ambiguity in a derivational way.Comment: This is a preprint of a paper to appear in: Journal of Language Modelling, 201

Static and Dynamic Vector Semantics for Lambda Calculus Models of Natural Language

To appear in Journal of Language Modelling. Short versions presented in DSALT 2016, SaLMoM 2016, LACL 2016. A version presented in AC 2017To appear in Journal of Language Modelling. Short versions presented in DSALT 2016, SaLMoM 2016, LACL 2016. A version presented in AC 2017To appear in Journal of Language Modelling. Short versions presented in DSALT 2016, SaLMoM 2016, LACL 2016. A version presented in AC 2017Vector models of language are based on the contextual aspects of language, the distributions of words and how they co-occur in text. Truth conditional models focus on the logical aspects of language, compositional properties of words and how they compose to form sentences. In the truth conditional approach, the denotation of a sentence determines its truth conditions, which can be taken to be a truth value, a set of possible worlds, a context change potential, or similar. In the vector models, the degree of co-occurrence of words in context determines how similar the meanings of words are. In this paper, we put these two models together and develop a vector semantics for language based on the simply typed lambda calculus models of natural language. We provide two types of vector semantics: a static one that uses techniques familiar from the truth conditional tradition and a dynamic one based on a form of dynamic interpretation inspired by Heim's context change potentials. We show how the dynamic model can be applied to entailment between a corpus and a sentence and we provide examples

A Comparison of Feature-Based and Neural Scansion of Poetry

Automatic analysis of poetic rhythm is a challenging task that involves linguistics, literature, and computer science. When the language to be analyzed is known, rule-based systems or data-driven methods can be used. In this paper, we analyze poetic rhythm in English and Spanish. We show that the representations of data learned from character-based neural models are more informative than the ones from hand-crafted features, and that a Bi-LSTM+CRF-model produces state-of-the art accuracy on scansion of poetry in two languages. Results also show that the information about whole word structure, and not just independent syllables, is highly informative for performing scansion.Comment: RANLP 201

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.Comment: In Proceedings of the the Conference on Empirical Methods on Natural Language Processing (EMNLP 2017). 2017. Copenhagen, Denmark. Association for Computational Linguistic

THE KNOWLEDGE OF PROSODY IN HELPING STUDENTS RESPONSE UTTERANCES APPROPRIATELY

In spoken discourse, besides the use of vocal and gestural signs, prosody is an important part to consider since without appropriate prosody- Intonation and accent and the meaningful elements of speech apart from the words that are uttered (Kreidler, 1998), the speaker may fail to deliver the meanings and the listeners may fail to catch the message. Then, it results in misunderstanding. This study is conducted to find out whether the knowledge of prosody helps students to communicate appropriately. The participants of this study are 20 students who joined semantics and 20 students who did not take semantics. They were asked to give appropriate respond to contrast the 20 utterance which have emphases. The utterances are recorded and transcribed. Triangulation was done by another person to get the accuracy of the data. The result shows that there are more students (85% ) who have knowledge of prosody able to respond the utterances appropriately. This proves that teaching prosody is essensial to make the students realize the function of prosody