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We report on experiments with the algorithm using the Bioscope corpus. Il lavoro affronta il problema di i- dentificare l ambito a cui si applica una negazione o un espressione dubitativa nel testo di un referto medico. Si propone un algoritmo di apprendimento automatico, che analizza l albero di parsing di ogni frase. Riportiamo infine i risultati di e- sperimenti con l algoritmo effettuati usando il corpus Bioscope.

Clinical notes are written in informal natural language, where, besides annotating evidence collected during a patient visit, physician report historical facts about the patient and suggested or discarded hypothesis. Annotations about dismissed hypotheses or evidence about the absence of a phenomenon are particularly abundant in these notes and should be recognized as such in order to avoid misleading conclusions.

A standard keyword based search engine might for example return many irrelevant documents where a certain symptom is mentioned but it does not affect the patient. Medical records are currently analysed by clinical experts, who read and annotate them manually. In some countries like Spain, it has become mandatory by law for all medical records to be annotated with the mentions of any relevant reported fact, associated with their official ICD9 code.

To assign the right ICD9 code, it is of critical importance to recognize the kind of context of each mention: In the BioScope corpus, a collection of bio-medical text, one out of eight sentences indeed contains negations Vincze et al. In order to automate the process of annotation of clinical notes, the following steps can be envisaged: Bodenreider, ; 3. NE recognition and normalization steps can be performed by relying on shallow analysis of texts for an exhaustive and updated overview of the state of the art, see Pradhan et al.

The identification of negative or speculative scope, instead, cannot just rely on such simple text analysis techniques, and would require identifying relations between parts, by means of a deeper syntactic-semantic analysis of sentences.

This work presents a novel algorithm that learns to determine the boundaries of negative and speculative scopes, by navigating the parse tree of a sentence and by exploiting machine learning techniques that rely on features extracted from the analysis of the parse tree.

For the detection of negative and speculative scope, both rulebased approaches and machine learning approaches have been proposed. They use an extended cue lexicon of medical conditions Chapman et al. They perform their analysis for English as well as for low resources languages, i. Their experiments show that lexical cues and contextual features are quite relevant for relation extraction i. Their system consists of two classifiers, one that decides which tokens in a sentence are negation signals, and another that finds the full scope of these negation signals.

On the Bioscope corpus, the first classifier achieves an F1 score of A second classifier is trained to determine, at the sentence level, which tokens are affected by the signals previously identified. The system was trained and evaluated on the clinical texts of the BioScope corpus. In the signal detection task, the classifier achieved an F1 score of In the scope detection task, a token was correctly classified if it had been properly identified as being inside or outside the scope of all the negation signals present in the sentence.

They achieved an F1 score of The process involves a first step of negative cue identification that exploits a binary classifier. The second step instead analyses the parse tree of each sentence and tries to identify possible candidates for a negative scope extracted with a heuristics: A classifier is then trained to recognize whether any of these candidates falls within the scope of the cue.

The system was trained on a Chinese corpus manually annotated including scientific literature and financial articles. At prediction time, besides the classifier, also a set of rules based on a suitable lexicon is used to filter the candidates and to assign them to the scope of a cue.

Since the classifier operates independently on each candidate, it may happen that a set of discontiguous candidates is selected. A final clean up step is hence applied to combine them. The cui exploits morphological features, attribute and dictionary features. For scope detection, we implemented a novel algorithm that explores the parse tree of the sentence, as detailed in the following.

Example of parse tree with a negative scope. Our approach is however different from the one by Zou et al. Our approach assumes that scopes are contiguous and they contain the cue. Hence, instead of assembling candidates independently of each other, our process starts from a cue and tries to expand it as far as possible with contiguous subtrees either towards the left or towards the right.

In the description of the algorithm, we will use the following definitions. Symmetrical of Left adjacency list. The algorithm for computing the scope S of a cue token at position c in the sentence, exploits the definitions of RAL and LAL and is described below.

In essence, the algorithm moves first towards the left as far as possible, and whenever it adds a node in step 2, it also adds all its right children, in order to ensure that the scope remains contiguous. It then repeats the same process towards the right. Assuming that the parse tree of the sentence is non-projective, the algorithm produces a scope S consisting of consecutive tokens of the sentence. The proof descends from the properties of nonprojective trees.

The decision on whether a candidate belongs to a scope is entrusted to a binary classifier which is trained on the corpus, using features from the nodes in the context of the candidate. These are nodes selected from the parse tree. In particular, there will be two cases to consider, depending on the current step of the algorithm. For example, in step 2 the nodes considered are illustrated in Figure 1. Below we show which nodes are considered for feature extraction in step 3: We illustrate which nodes the algorithm would visit, on the parse tree of Figure 0.

The negative cue is given by the token no, marked in grey in the figure. The word with largest index in LAL is, it is not within the scope, hence S stays the same and we proceed to step 3. The next token is level, which is part of the scope: The algorithm always terminates with a contiguous sequence of tokens in S that include the cue. Notice that differently from Zou et al. We pre-processed a subset of the corpus for a total of sentences, with the Tanl pipeline Attardi et al.

In order to prepare the training corpus, the BioScope corpus was pre-processed as follows.


We applied the Tanl linguistic pipeline in order to split the documents into sentences and to perform tokenization according to the Penn Treebank Taylor et al. The annotations from BioScope were integrated back into the pre-processed format using an IOB notation Speranza, If a token is part of more then one scope, the id of the cue of each scope is listed, separated by comma. Here is an example of annotated sentence: For the cue detection task, we experimented with three classifiers: Tanl NER Attardi et al.

DeepNL also provides code for creating word embeddings from text using either the Language Model approach by Collobert et al. The features provided to classifiers 1 and 2 included morphological features, lexical features i. The solution based on DeepNL reduces the burden of feature selection since it uses word embeddings as features, which can be learned through unsupervised techniques from plain text; in the experiments, we exploited the word embedding from Collobert et al.

Besides word embeddings, also discrete features are used: The best results achieved on the test set, with the above mentioned classifier, are reported in Table 1. Precision Recall F1 LibLinear The classifier, used in the algorithm of scope detection for deciding whether a candidate be- 17 20 longs to a scope or not, is a binary classifier, implemented using liblinear.

The performance of the scope detection algorithm is measured also in terms of Percentage of Correct Scopes PCSa measure that considers a predicted scope correct if it matches exactly the correct scope.

The results achieved on our test set from the BioScope corpus are reported in Table 2. Speculation scope detection We can note a significant improvement in Recall, that leads also to an relevant improvement in F1. The scope detection step exploits the structure of sentences as represented by its dependency parse tree.

The novelty with respect to previous approaches also exploiting dependency parses is that the tree is used as a guide in the choice of how to extend the current scope.

This avoids producing spurious scopes, for example discontiguous ones. The algorithm also may gather partial subtrees of the parse.

This provides more resilience and flexibility. The accuracy of the algorithm of course depends on the accuracy of the dependency parser, both in the production of the training corpus and in the analysis. We used a fast transitionbased dependency parser trained on the Genia corpus, which turned out to be adequate for the task.

Indeed in experiments on the BioScope corpus the algorithm achieved accuracy scores above the state of the art. Giuseppe Attardi et al a. Giuseppe Attardi, et al.

Proceedings of the Second Italian Conference - PDF

Nucleic Acids Research, vol. Journal of Machine Learning Research, 12, N.

Advisory Council Meeting, 25 September 2014, Paris

Journal of the American society for information science and technology, A library for large linear classification. Dowling, Tyler Thornblade, and Wendy W. An algorithm for determining negation, experiencer, and temporal status from clinical reports.

Analysis of Clinical Text. Chute Dependency parser-based negation detection in clinical narratives. BMC bioinformatics, 9 Suppl 11S9. Negation and Speculation Identification in Chinese Language. Distributional Semantic Models DSM that represent words as vectors of weights over a high dimensional feature space have proved very effective in representing semantic or syntactic word similarity.

For certain tasks however it is important to represent contrasting aspects such as polarity, opposite senses or idiomatic use of words.

We present a method for computing discriminative word embeddings can be used in sentiment classification or any other task where one needs to discriminate between contrasting semantic aspects. We present an experiment in the identification of reports on natural disasters in tweets by means of these embeddings.

Incorporating such representations has allowed improving many natural language tasks. They also reduce the burden of feature selection since these models can be learned through unsupervised techniques from plain text.

Proceedings of the Second Italian Conference

Deep learning algorithms for NLP tasks exploit distributional representation of words. Traditional embeddings are created from large collections of unannotated documents through unsupervised learning, for example building a neural language model Collobert et al.

These embeddings are suitable to represent syntactic similarity, which can be measured through the Euclidean distance in the embeddings space. They are not appropriate though to represent semantic dissimilarity, since for example antonyms end up at close distance in the embeddings space In this paper we explore a technique for building discriminative word embeddings, which incorporate semantic aspects that are not directly 20 23 obtainable from textual collocations.

In particular, such embedding can be useful in sentiment classification in order to learn vector representations where words of opposite polarity are distant from each other. For creating the embeddings, we used DeepNL 1, a library for building NLP applications based on a deep learning architecture. DeepNL provides two methods for building embeddings, one is based on the use of a neural language model, as proposed by Collobert et al.

The neural language method can be hard to train and the process is often quite time consuming, since several iterations are required over the whole training set. Some researcher provide precomputed embeddings for English 2. An optimistic approach to matrix updates is also exploited to avoid synchronization costs. Levy and Goldberg have shown similarly that the skip-gram model by Mikolov et al. One needs to learn a vector representation where words of opposite polarity are distant.

The original hinge loss function in the algorithm by Collobert et al. A second loss function is introduced as objective for minimization: The overall hinge loss is a linear combination of the two: The DeepNL library provides a training algorithm for discriminative word embedding that performs gradient descent using an adaptive learning rate according to the AdaGrad method. The algorithm requires a training set consisting of documents annotated with their discriminative value, for example a corpus of tweets with their sentiment polarity, or in general documents with 21 24 multiple class tags.

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