Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Toward higher effectiveness for recall-oriented information retrieval: A patent retrieval case study

Research in information retrieval (IR) has largely been directed towards tasks requiring high precision. Recently, other IR applications which can be described as recall-oriented IR tasks have received increased attention in the IR research domain. Prominent among these IR applications are patent search and legal search, where users are typically ready to check hundreds or possibly thousands of documents in order to find any possible relevant document. The main concerns in this kind of application are very different from those in standard precision-oriented IR tasks, where users tend to be focused on finding an answer to their information need that can typically be addressed by one or two relevant documents. For precision-oriented tasks, mean average precision continues to be used as the primary evaluation metric for almost all IR applications. For recall-oriented IR applications the nature of the search task, including objectives, users, queries, and document collections, is different from that of standard precision-oriented search tasks. In this research study, two dimensions in IR are explored for the recall-oriented patent search task. The study includes IR system evaluation and multilingual IR for patent search. In each of these dimensions, current IR techniques are studied and novel techniques developed especially for this kind of recall-oriented IR application are proposed and investigated experimentally in the context of patent retrieval. The techniques developed in this thesis provide a significant contribution toward evaluating the effectiveness of recall-oriented IR in general and particularly patent search, and improving the efficiency of multilingual search for this kind of task

A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

Application of pre-training and fine-tuning AI models to machine translation: a case study of multilingual text classification in Baidu

With the development of international information technology, we are producing a huge amount of information all the time. The processing ability of information in various languages is gradually replacing information and becoming a rarer resource. How to obtain the most effective information in such a large and complex amount of multilingual textual information is a major goal of multilingual information processing. Multilingual text classification helps users to break the language barrier and accurately locate the required information and triage information. At the same time, the rapid development of the Internet has accelerated the communication among users of various languages, giving rise to a large number of multilingual texts, such as book and movie reviews, online chats, product introductions and other forms, which contain a large amount of valuable implicit information and urgently need automated tools to categorize and process those multilingual texts. This work describes the Natural Language Process (NLP) sub-task known as Multilingual Text Classification (MTC) performed within the context of Baidu, a Chinese leading AI company with a strong Internet base, whose NLP division led the industry in deep learning technology to go online in Machine Translation (MT) and search. Multilingual text classification is an important module in NLP machine translation and a basic module in NLP tasks. It can be applied to many fields, such as Fake Reviews Detection, News Headlines Categories Classification, Analysis of positive and negative reviews and so on. In the following work, we will first define the AI model paradigm of 'pre-training and fine-tuning' in deep learning in the Baidu NLP department. Then investigated the application scenarios of multilingual text classification. Most of the text classification systems currently available in the Chinese market are designed for a single language, such as Alibaba's text classification system. If users need to classify texts of the same category in multiple languages, they need to train multiple single text classification systems and then classify them one by one. However, many internationalized products do not have a single text language, such as AliExpress cross-border e-commerce business, Airbnb B&B business, etc. Industry needs to understand and classify users’ reviews in various languages, and have conducted in-depth statistics and marketing strategy development, and multilingual text classification is particularly important in this scenario. Therefore, we focus on interpreting the methodology of multilingual text classification model of machine translation in Baidu NLP department, and capture sets of multilingual data of reviews, news headlines and other data for manual classification and labeling, use the labeling results for fine-tuning of multilingual text classification model, and output the quality evaluation data of Baidu multilingual text classification model after fine-tuning. We will discuss if the pre-training and fine-tuning of the large model can substantially improve the quality and performance of multilingual text classification. Finally, based on the machine translation-multilingual text classification model, we derive the application method of pre-training and fine-tuning paradigm in the current cutting-edge deep learning AI model under the NLP system and verify the generality and cutting-edge of the pre-training and fine-tuning paradigm in the deep learning-intelligent search field.Com o desenvolvimento da tecnologia de informação internacional, estamos sempre a produzir uma enorme quantidade de informação e o recurso mais escasso já não é a informação, mas a capacidade de processar informação em cada língua. A maior parte da informação multilingue é expressa sob a forma de texto. Como obter a informação mais eficaz numa quantidade tão considerável e complexa de informação textual multilingue é um dos principais objetivos do processamento de informação multilingue. A classificação de texto multilingue ajuda os utilizadores a quebrar a barreira linguística e a localizar com precisão a informação necessária e a classificá-la. Ao mesmo tempo, o rápido desenvolvimento da Internet acelerou a comunicação entre utilizadores de várias línguas, dando origem a um grande número de textos multilingues, tais como críticas de livros e filmes, chats, introduções de produtos e outros distintos textos, que contêm uma grande quantidade de informação implícita valiosa e necessitam urgentemente de ferramentas automatizadas para categorizar e processar esses textos multilingues. Este trabalho descreve a subtarefa do Processamento de Linguagem Natural (PNL) conhecida como Classificação de Texto Multilingue (MTC), realizada no contexto da Baidu, uma empresa chinesa líder em IA, cuja equipa de PNL levou a indústria em tecnologia baseada em aprendizagem neuronal a destacar-se em Tradução Automática (MT) e pesquisa científica. A classificação multilingue de textos é um módulo importante na tradução automática de PNL e um módulo básico em tarefas de PNL. A MTC pode ser aplicada a muitos campos, tais como análise de sentimentos multilingues, categorização de notícias, filtragem de conteúdos indesejados (do inglês spam), entre outros. Neste trabalho, iremos primeiro definir o paradigma do modelo AI de 'pré-treino e afinação' em aprendizagem profunda no departamento de PNL da Baidu. Em seguida, realizaremos a pesquisa sobre outros produtos no mercado com capacidade de classificação de texto — a classificação de texto levada a cabo pela Alibaba. Após a pesquisa, verificamos que a maioria dos sistemas de classificação de texto atualmente disponíveis no mercado chinês são concebidos para uma única língua, tal como o sistema de classificação de texto Alibaba. Se os utilizadores precisarem de classificar textos da mesma categoria em várias línguas, precisam de aplicar vários sistemas de classificação de texto para cada língua e depois classificá-los um a um. No entanto, muitos produtos internacionalizados não têm uma única língua de texto, tais como AliExpress comércio eletrónico transfronteiriço, Airbnb B&B business, etc. A indústria precisa compreender e classificar as revisões dos utilizadores em várias línguas. Esta necessidade conduziu a um desenvolvimento aprofundado de estatísticas e estratégias de marketing, e a classificação de textos multilingues é particularmente importante neste cenário. Desta forma, concentrar-nos-emos na interpretação da metodologia do modelo de classificação de texto multilingue da tradução automática no departamento de PNL Baidu. Colhemos para o efeito conjuntos de dados multilingues de comentários e críticas, manchetes de notícias e outros dados para classificação manual, utilizamos os resultados dessa classificação para o aperfeiçoamento do modelo de classificação de texto multilingue e produzimos os dados de avaliação da qualidade do modelo de classificação de texto multilingue da Baidu. Discutiremos se o pré-treino e o aperfeiçoamento do modelo podem melhorar substancialmente a qualidade e o desempenho da classificação de texto multilingue. Finalmente, com base no modelo de classificação de texto multilingue de tradução automática, derivamos o método de aplicação do paradigma de pré-formação e afinação no atual modelo de IA de aprendizagem profunda de ponta sob o sistema de PNL, e verificamos a robustez e os resultados positivos do paradigma de pré-treino e afinação no campo de pesquisa de aprendizagem profunda

Complex Innovation and the Patent Office

As the universe of available information becomes larger and innovation becomes more complex, the task of examining patent applications becomes increasingly difficult. This Article argues that the United States Patent Office has insufficiently responded to changes in the information universe and to innovation norms. This leaves the Patent Office less able to adequately assess patent applications, and more likely to grant bad patents. After first demonstrating how innovation has been responsive to contemporary innovation norms for hundreds of years, this Article uses information and data science methods to empirically demonstrate how innovation has drastically changed in recent decades. After empirically demonstrating the changed innovation system and the inadequate response to these changes by the USPTO, this Article concludes with policy prescriptions aimed to help the Patent Office implement examination procedures adequate to assess 21st century innovation. These prescriptions include more granular crediting for the time spent by examiners assessing applications, an increased focus on teamwork at the Patent Office, improvements to the inter partes review process, and alterations to the analogous art doctrine

Towards an Intellectual Property Rights Strategy for Innovation in Europe

On October 13, 2009 the Science and Technology Options Assessment Panel (STOA) together with Knowledge4Innovation/The Lisbon Forum, supported by Technopolis Consulting Group and TNO, organised a half-day workshop entitled ‘Towards an Intellectual Property Rights Strategy for Innovation in Europe’. This workshop was part of the 1st European Innovation Summit at the European Parliament which took place on 13 October and 14 October 2009. It addressed the topics of the evolution and current issues concerning the European Patent System as well as International Protection and Enforcement of IPR (with special consideration of issues pertaining to IP enforcement in the Digital Environment). Conclusions drawn point to the benefits of a comprehensive European IPR strategy, covering a broad range of IP instruments and topics

Adaptation of machine translation for multilingual information retrieval in the medical domain

Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR. Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech–English, German–English, and French–English. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets. Results. The search query translation results achieved in our experiments are outstanding – our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech–English, from 23.03 to 40.82 for German–English, and from 32.67 to 40.82 for French–English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French–English. For Czech–English and German–English, the increased MT quality does not lead to better IR results. Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance – better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions