53 research outputs found
Esfinge at CLEF 2008: Experimenting with answer retrieval patterns. Can they help?
Esfinge is a general domain Portuguese question answering system which has been participating at QA@CLEF since 2004. It uses the information available in the ?official? document collections used in QA@CLEF (newspaper text and Wikipedia), but additionally it also uses information from the Web as an additional resource when searching for answers. Where it regards the use of external tools, Esfinge uses a syntactic analyzer, a morphological analyzer and a named entity recognizer. This year an alternative approach to retrieve answers was tested: whereas in previous years, search patterns were used to retrieve relevant documents, this year a new type of search patterns was also used to extract the answers themselves. Besides that we took advantage of the main novelty introduced this year by QA@CLEF organization which was that the systems could return up to three answers for each question, instead of the single answer allowed in previous editions. This enabled the investigation about how good were the second and third best answers returned by Esfinge (when the first answer is not correct). The experiments revealed that the answer retrieval patterns created for this participation improve the results, but only for definition questions. Regarding the study of the three answers returned by Esfinge, the conclusion was that when Esfinge answers correctly a question, it does so usually with its first answer
Rapport : a fact-based question answering system for portuguese
Question answering is one of the longest-standing problems in natural language processing. Although natural language interfaces for computer systems can be considered
more common these days, the same still does not happen regarding access to specific
textual information. Any full text search engine can easily retrieve documents containing user specified or closely related terms, however it is typically unable to answer user
questions with small passages or short answers.
The problem with question answering is that text is hard to process, due to its syntactic structure and, to a higher degree, to its semantic contents. At the sentence level,
although the syntactic aspects of natural language have well known rules, the size and
complexity of a sentence may make it difficult to analyze its structure. Furthermore, semantic aspects are still arduous to address, with text ambiguity being one of the hardest
tasks to handle. There is also the need to correctly process the question in order to define its target, and then select and process the answers found in a text. Additionally, the
selected text that may yield the answer to a given question must be further processed
in order to present just a passage instead of the full text. These issues take also longer
to address in languages other than English, as is the case of Portuguese, that have a lot
less people working on them.
This work focuses on question answering for Portuguese. In other words, our field
of interest is in the presentation of short answers, passages, and possibly full sentences,
but not whole documents, to questions formulated using natural language. For that purpose, we have developed a system, RAPPORT, built upon the use of open information
extraction techniques for extracting triples, so called facts, characterizing information
on text files, and then storing and using them for answering user queries done in natural language. These facts, in the form of subject, predicate and object, alongside other
metadata, constitute the basis of the answers presented by the system. Facts work both
by storing short and direct information found in a text, typically entity related information, and by containing in themselves the answers to the questions already in the
form of small passages. As for the results, although there is margin for improvement,
they are a tangible proof of the adequacy of our approach and its different modules for
storing information and retrieving answers in question answering systems.
In the process, in addition to contributing with a new approach to question answering for Portuguese, and validating the application of open information extraction to
question answering, we have developed a set of tools that has been used in other natural language processing related works, such as is the case of a lemmatizer, LEMPORT,
which was built from scratch, and has a high accuracy. Many of these tools result from
the improvement of those found in the Apache OpenNLP toolkit, by pre-processing their
input, post-processing their output, or both, and by training models for use in those
tools or other, such as MaltParser. Other tools include the creation of interfaces for
other resources containing, for example, synonyms, hypernyms, hyponyms, or the creation of lists of, for instance, relations between verbs and agents, using rules
A study of the use of natural language processing for conversational agents
Language is a mark of humanity and conscience, with the conversation (or dialogue) as one of the most fundamental manners of communication that we learn as children. Therefore one way to make a computer more attractive for interaction with users is through the use of natural language. Among the systems with some degree of language capabilities developed, the Eliza chatterbot is probably the first with a focus on dialogue. In order to make the interaction more interesting and useful to the user there are other approaches besides chatterbots, like conversational agents. These agents generally have, to some degree, properties like: a body (with cognitive states, including beliefs, desires and intentions or objectives); an interactive incorporation in the real or virtual world (including perception of events, communication, ability to manipulate the world and communicate with others); and behavior similar to a human (including affective abilities). This type of agents has been called by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is responsible for translating the user’s speech into text. (2) The Natural Language Understanding component produces a semantic representation suitable for dialogues, usually using grammars and ontologies. (3) The Task Manager chooses the concepts to be expressed to the user. (4) The Natural Language Generation component defines how to express these concepts in words. (5) The dialog manager controls the structure of the dialogue. (6) The synthesizer is responsible for translating the agents answer into speech. However, there is no consensus about the necessary resources for developing conversational agents and the difficulties involved (especially in resource-poor languages). This work focuses on the influence of natural language components (dialogue understander and manager) and analyses, in particular the use of parsing systems as part of developing conversational agents with more flexible language capabilities. This work analyses what kind of parsing resources contributes to conversational agents and discusses how to develop them targeting Portuguese, which is a resource-poor language. To do so we analyze approaches to the understanding of natural language, and identify parsing approaches that offer good performance, based on which we develop a prototype to evaluate the impact of using a parser in a conversational agent.linguagem é uma marca da humanidade e da consciência, sendo a conversação (ou diálogo) uma das maneiras de comunicacão mais fundamentais que aprendemos quando crianças. Por isso uma forma de fazer um computador mais atrativo para interação com usuários é usando linguagem natural. Dos sistemas com algum grau de capacidade de linguagem desenvolvidos, o chatterbot Eliza é, provavelmente, o primeiro sistema com foco em diálogo. Com o objetivo de tornar a interação mais interessante e útil para o usuário há outras aplicações alem de chatterbots, como agentes conversacionais. Estes agentes geralmente possuem, em algum grau, propriedades como: corpo (com estados cognitivos, incluindo crenças, desejos e intenções ou objetivos); incorporação interativa no mundo real ou virtual (incluindo percepções de eventos, comunicação, habilidade de manipular o mundo e comunicar com outros agentes); e comportamento similar ao humano (incluindo habilidades afetivas). Este tipo de agente tem sido chamado de diversos nomes como agentes animados ou agentes conversacionais incorporados. Um sistema de diálogo possui seis componentes básicos. (1) O componente de reconhecimento de fala que é responsável por traduzir a fala do usuário em texto. (2) O componente de entendimento de linguagem natural que produz uma representação semântica adequada para diálogos, normalmente utilizando gramáticas e ontologias. (3) O gerenciador de tarefa que escolhe os conceitos a serem expressos ao usuário. (4) O componente de geração de linguagem natural que define como expressar estes conceitos em palavras. (5) O gerenciador de diálogo controla a estrutura do diálogo. (6) O sintetizador de voz é responsável por traduzir a resposta do agente em fala. No entanto, não há consenso sobre os recursos necessários para desenvolver agentes conversacionais e a dificuldade envolvida nisso (especialmente em línguas com poucos recursos disponíveis). Este trabalho foca na influência dos componentes de linguagem natural (entendimento e gerência de diálogo) e analisa em especial o uso de sistemas de análise sintática (parser) como parte do desenvolvimento de agentes conversacionais com habilidades de linguagem mais flexível. Este trabalho analisa quais os recursos do analisador sintático contribuem para agentes conversacionais e aborda como os desenvolver, tendo como língua alvo o português (uma língua com poucos recursos disponíveis). Para isto, analisamos as abordagens de entendimento de linguagem natural e identificamos as abordagens de análise sintática que oferecem um bom desempenho. Baseados nesta análise, desenvolvemos um protótipo para avaliar o impacto do uso de analisador sintático em um agente conversacional
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