Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

語用論(Pragmatics)を考える

Transactions of the Institute of British Geographers

We present a novel technique that automatically alerts authors of requirements to the presence of potentially dangerous ambiguities. We first establish the notion of nocuous ambiguities, which are those that are likely to lead to misunderstandings. We test our approach on coordination ambiguities, which occur when words such as and and or are used. Our starting point is a dataset of ambiguous phrases from a requirements corpus and associated human judgements about their interpretation. We then use heuristics, based largely on word distribution information, to automatically replicate these judgements. The heuristics eliminate ambiguities which people interpret easily, leaving the nocuous ones to be analysed and rewritten by hand. We report on a series of experiments that evaluate our heuristics' performance against the human judgements. Many of our heuristics achieve high precision, and recall is greatly increased when they are used in combination.

/pdf/identifying-nocuous-ambiguities-in-natural-language-167qooqide.pdf

Identifying Nocuous Ambiguities in Natural Language Requirements

Many requirements documents are written in natural language (NL). However, with the flexibility of NL comes the risk of introducing unwanted ambiguities in the requirements and misunderstandings between stakeholders. In this paper, we describe an automated approach to identify potentially nocuous ambiguity, which occurs when text is interpreted differently by different readers. We concentrate on anaphoric ambiguity, which occurs when readers may disagree on how pronouns should be interpreted. We describe a number of heuristics, each of which captures information that may lead a reader to favor a particular interpretation of the text. We use these heuristics to build a classifier, which in turn predicts the degree to which particular interpretations are preferred. We collected multiple human judgements on the interpretation of requirements exhibiting anaphoric ambiguity and showed how the distribution of these judgements can be used to assess whether a particular instance of ambiguity is nocuous. Given a requirements document written in natural language, our approach can identify sentences that contain anaphoric ambiguity, and use the classifier to alert the requirements writer of text that runs the risk of misinterpretation. We report on a series of experiments that we conducted to evaluate the performance of the automated system we developed to support our approach. The results show that the system achieves high recall with a consistent improvement on baseline precision subject to some ambiguity tolerance levels, allowing us to explore and highlight realistic and potentially problematic ambiguities in actual requirements documents.

/pdf/analysing-anaphoric-ambiguity-in-natural-language-290s89zewk.pdf

Analysing anaphoric ambiguity in natural language requirements

This paper pays attention to the immense and febrile field of digital image files which picture the smart city as they circulate on the social media platform Twitter. The paper considers tweeted im...

/pdf/seeing-the-smart-city-on-twitter-colour-and-the-affective-zv84c6ywua.pdf

Seeing the smart city on Twitter: Colour and the affective territories of becoming smart:

We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.

/pdf/a-hybrid-model-for-automatic-emotion-recognition-in-suicide-1hc011q4rj.pdf

A hybrid model for automatic emotion recognition in suicide notes.

The use of tacit knowledge is a common feature in everyday communication. It allows people to communicate effectively without forcing them to make everything tediously and painstakingly explicit, provided they all share a common understanding of whatever is not made explicit. If this latter criterion does not hold, confusion and misunderstanding will ensue. Tacit knowledge is also commonplace in requirements where it also affords economy of expression. However, the use of tacit knowledge also suffers from the same risk of misunderstanding, with the associated problems of anticipating where it has the potential for confusion and of unravelling where it has played an actual role in misunderstanding. Thus, the effective communication of requirements knowledge (whether verbally, through a document or some other medium) requires an understanding of what knowledge is and isn’t (necessarily) held in common. This is very hard to get right as people from different professional and cultural backgrounds are typically involved. At its worst, tacit requirements knowledge may lead to software that fails to satisfy the customer’s requirements. In this chapter, we review the diverse views of tacit knowledge discussed in the literature from a wide range of disciplines, reflect on their commonalities and differences and propose a conceptual framework for requirements engineering that characterises the different facets of tacit knowledge that distinguish the different views. We then identify methodological and technical challenges for future research on the role of tacit knowledge in requirements engineering.

Alistair Willis

Papers

Identifying Nocuous Ambiguities in Natural Language Requirements

Analysing anaphoric ambiguity in natural language requirements

Seeing the smart city on Twitter: Colour and the affective territories of becoming smart:

A hybrid model for automatic emotion recognition in suicide notes.

Unpacking Tacit Knowledge for Requirements Engineering