REL: a Rapidly Extensible Language System II. REL English

REL, a Rapidly Extensible Language System, is an integrated information system operating in conversational interaction with the computer. It is intended for work with large or small data bases by means of highly individualized languages. The architecture of REL is based on theoretical assumptions about human information dynamics [I], among them the expanding process of conceptualization in working with data, and the idiosyncratic language use of the individual workers. The result of these assumptions is a system which allows the construction of highly individualized languages which are closely knit with the structure of the data and which can be rapidly extended and augmented with new concepts and structures through a facile definitional capability. The REL language processor is designed to accommodate a variety of languages whose structural characteristics may be considerably divergent. The REL English is one of the languages within the REL system. It is intended to facilitate sophisticated work with computers without the need for mastering programming languages. The structural power of REL English matches the extremely flexible organization of data in ring forms. Extensions of the basic REL English language can be achieved either through defining new concepts and structures in terms of the existing ones or through addition of new rules

REL: a Rapidly Extensible Language System II. REL English

REL, a Rapidly Extensible Language System, is an integrated information system operating in conversational interaction with the computer. It is intended for work with large or small data bases by means of highly individualized languages. The architecture of REL is based on theoretical assumptions about human information dynamics [I], among them the expanding process of conceptualization in working with data, and the idiosyncratic language use of the individual workers. The result of these assumptions is a system which allows the construction of highly individualized languages which are closely knit with the structure of the data and which can be rapidly extended and augmented with new concepts and structures through a facile definitional capability. The REL language processor is designed to accommodate a variety of languages whose structural characteristics may be considerably divergent. The REL English is one of the languages within the REL system. It is intended to facilitate sophisticated work with computers without the need for mastering programming languages. The structural power of REL English matches the extremely flexible organization of data in ring forms. Extensions of the basic REL English language can be achieved either through defining new concepts and structures in terms of the existing ones or through addition of new rules

Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection

Encoder-decoder models provide a generic architecture for sequence-to-sequence tasks such as speech recognition and translation. While offline systems are often evaluated on quality metrics like word error rates (WER) and BLEU, latency is also a crucial factor in many practical use-cases. We propose three latency reduction techniques for chunk-based incremental inference and evaluate their efficiency in terms of accuracy-latency trade-off. On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by sacrificing 1% WER (6% rel.) compared to offline transcription. Although our experiments use the Transformer, the hypothesis selection strategies are applicable to other encoder-decoder models. To avoid expensive re-computation, we use a unidirectionally-attending encoder. After an adaptation procedure to partial sequences, the unidirectional model performs on-par with the original model. We further show that our approach is also applicable to low-latency speech translation. On How2 English-Portuguese speech translation, we reduce latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5% rel.) compared to the offline system

Dublin City University at CLEF 2004: experiments in monolingual, bilingual and multilingual retrieval

The Dublin City University group participated in the monolingual, bilingual and multilingual retrieval tasks this year. The main focus of our investigation this year was extending our retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Results from our French monolingual experiments indicate that working in French is more effective for retrieval than adopting document and topic translation to English. However, comparison of our multilingual retrieval results using different topic and document translation reveals that this result does not extend to retrieved list merging for the multilingual task in a simple predictable way

Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings

We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. To tune the parameters of this model, we generate self-induced spelling error corpora. We perform our experiments for two languages. For English, we greatly outperform off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of a noisy channel model, showing that neural embeddings can be successfully exploited to improve upon the state-of-the-art. For Dutch, we also outperform an off-the-shelf spelling correction tool on manually annotated clinical records from the Antwerp University Hospital, but can offer no empirical evidence that our method counters the frequency bias of a noisy channel model in this case as well. However, both our context-sensitive model and our implementation of the noisy channel model obtain high scores on the test set, establishing a state-of-the-art for Dutch clinical spelling correction with the noisy channel model.Comment: Appears in volume 7 of the CLIN Journal, http://www.clinjournal.org/biblio/volum

Dublin City University at CLEF 2004: experiments with the ImageCLEF St Andrew's collection

For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness

Multi-Dimensional Inheritance

In this paper, we present an alternative approach to multiple inheritance for typed feature structures. In our approach, a feature structure can be associated with several types coming from different hierarchies (dimensions). In case of multiple inheritance, a type has supertypes from different hierarchies. We contrast this approach with approaches based on a single type hierarchy where a feature structure has only one unique most general type, and multiple inheritance involves computation of greatest lower bounds in the hierarchy. The proposed approach supports current linguistic analyses in constraint-based formalisms like HPSG, inheritance in the lexicon, and knowledge representation for NLP systems. Finally, we show that multi-dimensional inheritance hierarchies can be compiled into a Prolog term representation, which allows to compute the conjunction of two types efficiently by Prolog term unification.Comment: 9 pages, styles: a4,figfont,eepic,eps

A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation

When translating between two languages that differ in their degree of morpholog-ical synthesis, syntactic structures in one language may be realized as morphologi-cal structures in the other, and SMT mod-els need a mechanism to learn such trans-lations. Prior work has used morpheme splitting with flat representations that do not encode the hierarchical structure be-tween morphemes, but this structure is rel-evant for learning morphosyntactic con-straints and selectional preferences. We propose to model syntactic and morpho-logical structure jointly in a dependency translation model, allowing the system to generalize to the level of morphemes. We present a dependency representation of German compounds and particle verbs that results in improvements in transla-tion quality of 1.4–1.8 BLEU in the WMT English–German translation task.

Dublin City University at CLEF 2005: Experiments with the ImageCLEF St Andrew’s collection

The aim of the Dublin City University participation in the CLEF 2005 ImageCLEF St Andrew’s Collection task was to explore an alternative approach to exploiting text annotation and content-based retrieval in a novel combined way for pseudo relevance feedback (PRF). This method combines evidence from retrieved lists generated using text and content-based retrieval to determine which documents will be assumed relevant for the PRF process. Unfortunately the results show that while standard textbased PRF improves upon a no feedback text baseline, at present our new approach to combining evidence from text and content-based retrieval does not give further improve improvement

New England Faculty and College Students Differ in Their Views about Evolution, Creationism, Intelligent Design, and Religiosity

Public acceptance of evolution in Northeastern U.S. is the highest nationwide, only 59%. Here, we compare perspectives about evolution, creationism, intelligent design (ID), and religiosity between highly educated New England faculty (n = 244; 90% Ph.D. holders in 40 disciplines at 35 colleges/universities) and college students from public secular (n = 161), private secular (n = 298), and religious (n = 185) institutions: 94/3% of the faculty vs. 64/14% of the students admitted to accepting evolution openly and/or privately, and 82/18% of the faculty vs. 58/42% of the students thought that evolution is definitely true or probably true, respectively. Only 3% of the faculty vs. 23% of the students thought that evolution and creationism are in harmony. Although 92% of faculty and students thought that evolution relies on common ancestry, one in every four faculty and one in every three students did not know that humans are apes; 15% of the faculty vs. 34% of the students believed, incorrectly, that the origin of the human mind cannot be explained by evolution, and 30% of the faculty vs. 72% of the students was Lamarckian (believed in inheritance of acquired traits). Notably, 91% of the faculty was very concerned (64%) or somehow concerned (27%) about the controversy evolution vs creationism vs ID and its implications for science education: 96% of the faculty vs. 72% of the students supported the exclusive teaching of evolution while 4% of the faculty vs. 28% of the students favored equal time to evolution, creationism and ID; 92% of the faculty vs. 52% of the students perceived ID as not scientific and proposed to counter evolution or as doctrine consistent with creationism. Although ≈30% of both faculty and students considered religion to be very important in their lives, and ≈20% admitted to praying daily, the faculty was less religious (Religiosity Index faculty = 0.5 and students = 0.75) and, as expected, more knowledgeable about science (Science Index faculty = 2.27 and students = 1.60) and evolution (Evolution Index faculty = 2.48 and students = 1.65) than the students. Because attitudes toward evolution correlate (1) positively with understanding of science/evolution and (2) negatively with religiosity/political ideology, we conclude that science education combined with vigorous public debate should suffice to increase acceptance of naturalistic rationalism and decrease the negative impact of creationism and ID on society’s evolution literacy