Natural Language Processing Techniques

AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

Although geospatial question answering systems have received increasing attention in recent years, existing prototype systems struggle to properly answer qualitative spatial questions. In this work, we propose a unique framework for answering qualitative spatial questions, which comprises three main components: a geoparser that takes the input questions and extracts place semantic information from text, a reasoning system which is embedded with a crisp reasoner, and finally, answer extraction, which refines the solution space and generates final answers. We present an experimental design to evaluate our framework for point-based cardinal direction calculus (CDC) relations by developing an automated approach for generating three types of synthetic qualitative spatial questions. The initial evaluations of generated answers in our system are promising because a high proportion of answers were labelled correct.

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

We present Conditional Random Fields, a framework
\nfor building probabilistic models to segment
\nand label sequence data. Conditional random
\nfields offer several advantages over hidden
\nMarkov models and stochastic grammars
\nfor such tasks, including the ability to relax
\nstrong independence assumptions made in those
\nmodels. Conditional random fields also avoid
\na fundamental limitation of maximum entropy
\nMarkov models (MEMMs) and other discriminative
\nMarkov models based on directed graphical
\nmodels, which can be biased towards states
\nwith few successor states. We present iterative
\nparameter estimation algorithms for conditional
\nrandom fields and compare the performance of
\nthe resulting models to HMMs and MEMMs on
\nsynthetic and natural-language data.

Longman Grammar of Spoken and Written English

Introduction Since its publication in 1985, the outstanding 1,800-page Comprehensive Grammar of the English Language, by Randolph Quirk, Sidney Greenbaum, Geoffrey Leech, and Jan Svartvik, has been the definitive description of the grammar of English and an in-. dispensable reference for any research in the analysis or generation of English that attempts serious coverage of the syntactic phenomena of the language. The new Longman Grammar of Spoken and Written English, by Douglas Biber, Stig Johansson, Geoffrey Leech, Susan Conrad, and Edward Finegan, is an important complement to the earlier work, extending and sometimes revising the descriptions of Quirk et al., by means of an extensive corpus analysis by the five authors and their research assistants. Now, the bookshelf of any researcher in English linguistics is incomplete without both volumes. Like Quirk et al. (hereafter CGEL), Biber and his colleagues attempt a detailed description of all the syntactic phenomena of English. But

Computational analysis of present-day American English

Building a Large Annotated Corpus of English: The Penn Treebank

As a result of this grant, the researchers have now published oil CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, with over 3 million words of that material assigned skeletal grammatical structure. This material now includes a fully hand-parsed version of the classic Brown corpus. About one half of the papers at the ACL Workshop on Using Large Text Corpora this past summer were based on the materials generated by this grant.

Profile hidden Markov models.

The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.

Cohesion in English

Cohesion in English is concerned with a relatively neglected part of the linguistic system: its resources for text construction, the range of meanings that are speciffically associated with relating what is being spoken or written to its semantic environment. A principal component of these resources is 'cohesion'. This book studies the cohesion that arises from semantic relations between sentences. Reference from one to the other, repetition of word meanings, the conjunctive force of but, so, then and the like are considered. Further, it describes a method for analysing and coding sentences, which is applied to specimen texts.

Probabilistic topic models

Surveying a suite of algorithms that offer a solution to managing large document archives.

Recurrent neural network based language model

A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition

BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

AbstractThere is a failure mode in large language models that we do not have a good name for, and thatwe therefore tend not to treat seriously enough. It is not hallucination — the model is not assertingsomething false. It is not refusal — the model answers at length. It is the production of responses thatcarry the complete outward form of careful reasoning while the cognitive work that reasoning issupposed to represent has not, in any meaningful sense, occurred. We call this theatrical compliance,and we argue that it is, in practical terms, more dangerous than either of the failure modes thatcurrently dominate alignment research. This paper identifies the phenomenon, characterizes its fiveprincipal forms, explains the asymmetry that makes it particularly costly in high-stakes settings, andoutlines the design requirements for systems intended to resist it. We do not describe such a systemin detail here. Our goal is to establish theatrical compliance as a research problem in its own rightand to argue that addressing it requires instruments operating at a fundamentally different level ofabstraction than task-level prompting frameworks.Keywords: theatrical compliance, large language models, AI reasoning quality, cognitiveprocess evaluation, prompt engineering, metacognitive systems.