Automatic Mapping Clinical Notes to Medical - RMIT University

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ecognition in QA, so we will not use any sentence selection method that finds possible answers. The sentences remaining after the sentence selection phase are then analysed for named entities. All named entities found in the sentences are considered to be possible answers to the user question. Once all possible answers to the questions are found, the actual answer selection phase takes place. For this, the question is analysed, which provides information on what kind of answer is expected. This can be, for example, country, river, distance, person, etc. as described in Table 1. The set of possible answers is now considered preferring answers that match the question type. The best answer (i.e. with the highest score and matching the question type) is returned to the user, which finishes a typical question answering interaction. 4 Named Entity Recognition The ability of finding exact answers by the AnswerFinder system relies heavily on the quality of the named entity recognition performed on the sentences that are relevant to the user question. Finding all named entities in the sentences is therefore of utmost importance. Missing named entities may mean that the answer to the question cannot be recovered anymore. We have tried different NERs in the context of question answering. In addition to a general purpose NER, we have developed our own NER. Even though several high quality NERs are available, we thought it important to have full control over the NER to make it better suited for the task at hand. 4.1 ANNIE ANNIE is part of the Sheffield GATE (General Architecture for Text Engineering) system (Gaizauskas et al., 1996) and stands for “A Nearly- New IE system”. This architecture does much more than we need, but it is possible to only extract the NER part of it. Unfortunately, there is not much documentation on the NER in ANNIE. The named entity types found by ANNIE match up with the MUC types as described in Table 2. ANNIE was chosen as an example of a typical NER because it is freely available to the research community and the named entity types are a subset of the MUC types. 52 4.2 AFNER In addition to ANNIE’s NER, we also look at the results from the NER that is developed within the AnswerFinder project, called AFNER. 4.2.1 General Approach The NER process used in AFNER consists of two phases. The first phase uses hand-written regular expressions and gazetteers (lists of named entities that are searched for in the sentences). These information sources are combined with machine learning techniques in the second phase. AFNER first tokenises the given text, applies the regular expressions to each token, and searches for occurrences of the token in the gazetteers. Regular expression matches and list occurrences are used as features in the machine learning classifier. These features are used in combination with token specific features, as well as features derived from the text as a whole. Using a model generated from the annotated corpus, each token is classified as either the beginning of (‘B’) or in (‘I’) a particular type of named entity, or out (‘OUT’) of any named entity. The classified tokens are then appropriately combined into named entities. 4.2.2 First Phase — Regular Expressions and Gazetteers Regular expressions are useful for finding named entities following identifiable patterns, such as dates, times, monetary expressions, etc. As a result, the entities that can be discovered using regular expressions are limited. However, matching a particular regular expression is a key feature used in identifying entities of these particular types. Gazetteers are useful for finding commonly referenced names of people, places or organisations, but are by no means exhaustive. The purpose of combining lists with other features is to supplement the lists used. 4.2.3 Second Phase — Machine Learning The second phase involves the machine learning component of AFNER. The technique used is maximum entropy, and the implementation of the classifier is adapted from Franz Josef Och’s YAS- MET. 4 The system is trained on the Remedia Corpus (Hirschman et al., 1999), which contains annotations of named entities. The regular expression and gazetteer matches are used as features, in combination with others 4 http://www.fjoch.com/YASMET.html
pertaining to both individual tokens and tokens in context. Features of individual tokens include those such as capitalisation, alpha/numeric information, etc. Contextual features are those that identify a token amongst surrounding text, or relate to tokens in surrounding text. For example, whether a token is next to a punctuation mark or a capitalised word, or whether a token is always capitalised in a passage of text. Contextual features relating to global information have been used as described by Chieu and Ng (2002). In addition, features of previous tokens are included. The features are then passed to a maximum entropy classifier which, for every token, returns a list of probabilities of the token to pertain to each category. The categories correspond with each type of entity type prepended with ‘B’ and ‘I’, and a general ‘OUT’ category for tokens not in any entity. The list of entity types used is the same as in the MUC tasks (see Table 2). Preliminary experiments revealed that often the top two or three entity type probabilities have similar values. For this reason the final named entity labels are computed on the basis of the top n probabilities (provided that they meet a defined threshold), where n is a customisable limit. Currently, a maximum of 3 candidate types are allowed per token. Classified tokens are then combined according to their classification to produce the final list of named entities. We have experimented with two methods named single and multiple. For single type combination only one entity can be associated with a string, whereas for multiple type combination several entities can be associated. Also, the multiple type combination allows overlaps of entities. The multiple type combination aims at increasing recall at the expense of ambiguous labelling and decrease of precision. In the case of multiple type combination (see Figure 1 for an example), each label prepended with ‘B’ signals the beginning of a named entity of the relevant type, and each ‘I’ label continues a named entity if it is preceded by a ‘B’ or ‘I’ label of the same type. If an ‘I’ label does not appear after a ‘B’ classification, it is treated as a ‘B’ label. In addition, if a ‘B’ label is preceded by an ‘I’ label, it will be both added as a separate entity (with the previous entity ending) and appended to the previous entity. The single type combination (Figure 2) is im- 53 plemented by filtering out all the overlapping entities of the output of the multiple type combination. This is done by selecting the longest-spanning entity and discarding all substring or overlapping strings. If there are two entities associated with exactly the same string, the one with higher probability is chosen. The probability of a multi-token entity is computed by combining the individual token probabilities. Currently we use the geometric mean but we are exploring other possibilities. If Pi is the probability of token i and P1...n is the probability of the entire sentence, the geometric mean of the probabilities is computed as: 5 Results P1...n = e n i=1 log P i n To evaluate the impact of the quality of NER within the context of question answering, we ran the AnswerFinder system using each of the named entity recognisers, ANNIE, AFNERs and AFNERm. This section first explains the experimental setup we used, then shows and discusses the results. 5.1 Experimental setup To evaluate AnswerFinder we used the data available for participants of the QA track of the 2005 TREC competition-based conference 5 . This competition provides us with a nice setting to measure the impact of the NERs. We simply use the documents and questions provided during the TREC 2005 competition. To determine whether a document or text fragment contains the answer we use Ken Litkowsky’s answer patterns, also available at the TREC website. The questions in TREC 2005 are grouped by topic. The competition consisted of 75 topics, with a total of 530 questions. These questions are divided into three different types: factoid, list, and other. In this paper, we only consider the factoid questions, that is, questions that require a single fact as answer. List asks for a list of answers and other is answered by giving any additional information about the topic. There are 362 factoid questions in the question set. In the experiments, AnswerFinder uses the TREC data as follows. First, we apply docu- 5 http://trec.nist.gov
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