Over the last several years, speech-based question answering (QA) has become very popular in contrast to pure search engine based approaches on a desktop. Open-domain QA systems are now much more powerful and precise, and they can be used in speech applications. Speech-based question answering systems often rely on predefined grammars for speech understanding. In order to improve the coverage o...

A knowledge management system is more than an archive of textual documents; it provides context information, allowing to know which documents where used by people with a common goal. In the hypothesis that a set of textual documents with a common context can be assimilated to the long term memory of a human expert executor, we can use on them mining techniques inspired by the mechanic of human ...

Contextualized delivery of information is one of the many strengths of ubiquitous computing. It makes information actionable and helps us to better understand our situations. In the realm of healthcare, contextual information provides a terse but precise picture of the patient’s health situation. The patient context can have many facets, ranging from nutrition context over health heritage conte...

This paper presents novel methods for modeling numerical common sense: the ability to infer whether a given number (e.g., three billion) is large, small, or normal for a given context (e.g., number of people facing a water shortage). We first discuss the necessity of numerical common sense in solving textual entailment problems. We explore two approaches for acquiring numerical common sense. Bo...

我們所參與公開評測 NTCIR10 RITE-2[5]將文字蘊涵的研究分成兩種層面,首先是分兩 類(Binary Class, BC) ,任務的目標是單純判別 T1 與 T2 之間是否具有蘊涵關係。但句 子之間蘊涵關係並不能單純以有或沒有這麼簡單就區分開,NTCIR RITE 另外定義多類 (Multi Class, MC)這項任務,將句子之間的蘊涵分類為正向、雙向、矛盾、與獨立四種 關係。假設這個句子對具有蘊涵關係,但有可能兩個句子所包涵的資訊數量不同,造成 我們只能從其中一個句子推論出另一個句子的完整的意思,這樣的情況我們稱為兩個句 子間的蘊涵關係為正向蘊涵。反之兩個句子可以互相推論出另一個句子的含意,這樣的 情況我們就稱為雙向蘊涵關係。假設句子對之間沒有蘊涵關係,我們可以很合理認為兩 個句子所表達的意思不相同,但這並不完全正確的想法。可能兩個句子所包涵的資訊大 致相同只是少部份...

This paper describes the Recognizing Textual Entailment (RTE) system that our teams developed for TAC 2011. Our system combines the entailment score calculated by lexicallevel matching with the machine-learningbased filtering mechanism using various features obtained from lexical-level, chunk-level and predicate argument structure-level information. In the filtering mechanism, we try to discard...

In allegories polysemy relates not only to the context and the audience’s understanding but also to the structural characters of these texts. This paper investigates the function of structural and narrative properties in the creation of multiple interpretations of an allegory. Focusing on the events and following a unique story-line is the most important trait in helping to read the alleg...

The main goal of FBK-irst participation at RTE-4 was to experiment the use of combined specialized entailment engines, each addressing a specific phenomena relevant to entailment. The approach is motivated since textual entailment is due to the combination of several linguistic phenomena which interact among them in a quite complex way. We were driven by the following two considerations: (i) de...

This paper presents a methodology for a quantitative and qualitative evaluation of Textual Entailment systems. We take advantage of the decomposition of Text Hypothesis pairs into monothematic pairs, i.e. pairs where only one linguistic phenomenon at a time is responsible for entailment judgment, and propose to run TE systems over such datasets. We show that several behaviours of a system can b...