A Word Database for Natural Language Processing A Word Database for Natural Language Processing Brigitte Barnett Hubert Lehmann Magdalena Zoeppritz IBM Scientific Center, Tiergartenstral3e 15, 6900 He[.]
That is why, in the last years, politicians have a significant interest to have a two-way com- munication with their citizens, to discover their opinions and feelings about different ideas. Therefore, it is essential to allocate resources for sentiment analysis, which is also called opinion mining (one of the most active re- search areas in naturallanguageprocessing since early 2000 [Liu et al 2012]).
Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learn- ing has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for naturallanguageprocessing (NLP) tasks. With empirical experiments on sentiment anal- ysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhanc- ing model performances in various NLP tasks.
NaturalLanguageProcessing (NLP) is a general term for a wide range of tasks and methods related to automated understanding of human languages. In recent years, the amount of available diverse textual information has been growing rapidly, and specialized computer systems can cover ways of managing, sorting, filtering and processing this data more efficiently. As a larger goal, research in NLP aims to create systems that can also `understand' the meaning behind the text, extract relevant knowledge, organize it into easily accessible formats, and even discover latent or previously unknown information using inference. For example, the field of biomedical research can benefit from various text mining and information extraction techniques, as the number of published papers is increasing exponentially every year, yet it is vital to stay up to date with all the latest advancements. Research in Machine Learning (ML) focuses on the development of algorithms for automatically learning patterns and making predictions based on empirical data, and it offers useful approaches to many NLP problems. Machine learning techniques are commonly divided into three categories:
The most explanatory method for presenting what actually happens within a NaturalLanguageprocessing system is by means of the „levels of language‟ approach. This is also referred to as the synchronic model of language and is distinguished from the earlier sequential model, which hypothesizes that the levels of human languageprocessing follow one another in a strictly sequential manner. Psycholinguistic research suggests that languageprocessing is much more dynamic, as the levels can interact in a variety of orders. Introspection reveals that we frequently use information we gain from what is typically thought of as a higher level of processing to assist in a lower level of analysis. For example, the pragmatic knowledge that the document you are reading is about biology will be used when a particular word that has several possible senses (or meanings) is encountered, and the word will be interpreted as having the biology sense. Of necessity, the following description of levels will be presented sequentially. The key point here is that meaning is conveyed by each and every level of language and that since humans have been shown to use all levels of language to gain understanding, the more capable an NLP system is, the more levels of language it will utilize.
Tools like parser, language generator, language understanding system, morphological analyzer, tokenizer, stemmer etc. as are used in naturallanguageprocessing tasks across a range of platforms are themselves products of this emergent computational synergy, as they are developed through human efforts. But they can never replicate or simulate this emergent computational synergy; since if hypothetically any of them can at all, the simulated/replicated synergy has to be able to produce another tool/ tools just like itself and that product has to produce yet another and so on ad infinitum- an infinite regress. This is logically impossible. So what our advanced naturallanguageprocessing tools can achieve is only a fragment of what this emergent computational synergy is supposed to produce through individual or collective efforts. It is not just a constraint on naturallanguageprocessing tools; rather, it is a constraint on machines in general which will be the products or gifts of artificial intelligence.
1965 International Conference on Computational Linguistics A Heuristic Approach to Natural Language Processing 18 "1965 International Conference on Computational Linguistics" A Heuristic Approach to N[.]
As noted, until recently most of the interest about unstructured data has focused on Text Mining, Information Retrieval and topic classification tasks. In particular, textual data provided by Web users represents one of the most useful and interesting sources of unstructured information that can be currently found. Under this light, the entire field of NaturalLanguageProcessing evolves together with the increase of computational power and the discovery of new techniques which are aimed to interpret such textual information. In order to decide which kind of support should be incorporated into NLTK, a review of the range of existing approaches and corpora for Sentiment Analysis is required. This review should provide considerations in order to decide which elements will receive support during the implementation phase. Useful parameters would take into consideration aspects such as the maturity of proposed approaches, the availability of related resources, licensing terms and issues, and specific implementation requirements that could eventually hinder the feasibility of implementing the specific approach within NLTK. The most interesting results in the field of Sentiment Analysis are mainly obtained employing Machine Learning techniques or lexicon-based analyses. Machine Learning can be described as the process of automatically inferring patterns and structures from data, ideally providing as few as possible domain-specific instructions to the machine that has to accomplish the task. As we will see, it is still not possible to simply ask a machine to guess a pattern without providing it with some guidelines that react our assumptions: this extreme edibility is something that still has to be reached, especially if we take performance issues into consideration. However, a reasonable balance between this idea and very specific step-by-step instructions can still be reached thanks to Machine Learning and statistical methods. A. Pre-processing and feature extraction
Naturallanguageprocessing (NLP) is the capability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI). The history of NLP generally started in the 1950’s, although works can be found from the earlier periods. In 1950 Alan Turing published an article titled “COMPUTING MACH IN ERY A N D IN TE LL IGEN CE” , which is at present called the “TURING TEST” as a criterion of intelligence. . Starting in the late 1980s, however, there was a revolution in naturallanguageprocessing with the introduction of machine learning algorithms for languageprocessing Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if- then rules similar to existing hand-written rules. However, part-of-speech tagging introduced the use of hidden Markov models to naturallanguageprocessing, and increasingly, research has focused on statistical models, which
Word embedding is a feature learning technique which aims at mapping words from a vocab- ulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for capturing both syntactic and semantic information about words. Word embeddings, when used as the underlying input representation, have been shown to be a great asset for a large variety of naturallanguageprocessing (NLP) tasks. Recent techniques to obtain such word embeddings are mostly based on neural network language models (NNLM). In such systems, the word vectors are randomly initialized and then trained to predict optimally the contexts in which the corresponding words tend to appear. Because words occurring in similar contexts have, in general, similar meanings, their resulting word embeddings are semantically close after training. However, such architectures might be challenging and time-consuming to train. In this thesis, we are focusing on building simple models which are fast and efﬁcient on large-scale datasets. As a result, we propose a model based on counts for computing word embeddings. A word co-occurrence probability matrix can easily be obtained by directly counting the context words surrounding the vocabulary words in a large corpus of texts. The computation can then be drastically simpliﬁed by performing a Hellinger PCA of this matrix. Besides being simple, fast and intuitive, this method has two other advantages over NNLM. It ﬁrst provides a framework to infer unseen words or phrases. Secondly, all embedding dimensions can be obtained after a single Hellinger PCA, while a new training is required for each new size with NNLM. We evaluate our word embeddings on classical word tagging tasks and show that we reach similar performance than with neural network based word embeddings.
RESEARCH IN NATURAL LANGUAGE PROCESSING RESEARCH IN NATURAL LANGUAGE PROCESSING University of Pennsylvania Department of Computer and Information Science This a brief report publications FACULTY STUDE[.]
Despite the useful “universal” aspect of programming languages, these languages are still understood only by very few people, unlike the natural languages which are understood by all. The ability to turn natural into programming languages will even- tually decrease the gap between very few and all, and open the benefits of computer programming to a larger number of users. In this paper, we showed how current state- of-the-art techniques in naturallanguageprocessing can allow us to devise a system for naturallanguage programming that addresses both the descriptive and procedural programming paradigms. The output of the system consists of automatically generated program skeletons, which were shown to help non-expert programmers in their task of describing algorithms in a programmatic way. As it turns out, advances in naturallanguageprocessing helped the task of naturallanguage programming.
Default Reasoning in Natural Language Processing Default Reasoning in Natural Language Processing l i d Z E R N I K Artificial Intelligence P r o g r a m G E , Research and D e v e l o p m e n t C e n[.]
INTEGRATING SPEECH AND NATURAL LANGUAGE PROCESSING I N T E G R A T I N G S P E E C H A N D N A T U R A L L A N G U A G E P R O C E S S I N G Robert Moore, Fernando Pereira, and Hy Murveit SKI Internat[.]
White Paper on Natural Language Processing White Paper on Natural Language Processing Ralph Weiscbedel, Chairperson BBN Systems and Technologies Corporation Jaime Carbonell Carnegie Mellon University[.]
Kernel-based methods are a staple machine learning approach in NaturalLanguageProcessing (NLP). Frequentist kernel methods like the Support Vector Machine (SVM) pushed the state of the art in many NLP tasks, especially classification and regression. One interesting aspect of kernels is their ability to be defined directly on structured objects like strings, trees and graphs. This approach has the potential to move the modelling effort from feature engineering to kernel engineering. This is useful when we do not have much prior knowledge about how the data
This tutorial will be useful to research students working in naturallanguageprocessing and re- searchers who would like to explore machine learning, deep learning and sequential learning. The prerequisite knowledge includes calculus, lin- ear algebra, probability and statistics. This tuto- rial serves the objectives to introduce novices to major topics within deep Bayesian learning, moti- vate and explain a topic of emerging importance for naturallanguage understanding, and present a novel synthesis combining distinct lines of ma- chine learning work.
4. What are some of the interesting challenges of naturallanguageprocessing? This chapter is divided into sections that skip between two quite different styles. In the “computing with language” sections, we will take on some linguistically motivated programming tasks without necessarily explaining how they work. In the “closer look at Python” sections we will systematically review key programming concepts. We’ll flag the two styles in the section titles, but later chapters will mix both styles without being so up-front about it. We hope this style of introduction gives you an authentic taste of what will come later, while covering a range of elementary concepts in linguis- tics and computer science. If you have basic familiarity with both areas, you can skip to Section 1.5; we will repeat any important points in later chapters, and if you miss anything you can easily consult the online reference material at http://www.nltk.org/. If the material is completely new to you, this chapter will raise more questions than it answers, questions that are addressed in the rest of this book.