Mathieu Lafourcade
email: mathieu.lafourcade@lirmm.fr

Lionel Ramadier
email: lionel.ramadier@lirmm.fr

Semantic RelationExtraction with Semantic Patterns: Experiment on Radiology Report

Keywords: Relation Extraction, Semantic constraints, NLP

This work presents a practical system for indexing terms and relations from French radiology reports, called IMAIOS. In this paper, we present how semantic relations (causes, consequences, symptoms, locations, parts…) between medical terms can be extracted. For this purpose, we handcrafted some linguistic patterns from on a subset of our radiology report corpora. As semantic patterns (de (of)) may be too general or ambiguous, semantic constraints have been added. For instance, in the sentence néoplasie du sein (neoplasm of breast) the system knowing neoplasm as a disease and breast as an anatomical location, identify the relation as being a location: neoplasm r-lieu breast. An evaluation of the effect of semantic constraints is proposed.

Introduction

In the domain of radiology, the amount of pictures and textual reports is growing at an unprecedented rate. This quantity of medical information exceeds the ability of the radiologist to manage it effectively. One challenge is then to figure how to make computer help to effectively use these findings and to improve the management of patients. NLP methods allow for enhanced indexing and searching of medical reports with the linking of relevant terms through semantic relations and enabling on the fly inference of new knowledge. This work presents a practical system for indexing terms and relations from French radiology reports, called IMAIOS. The main task of this system is to extract semantic relations from unstructured text that can be integrated in a lexical-semantic network in order to improve the quality of the latter. The IMAIOS system differs from other techniques by the use of the french lexical-semantic JeuxDeMots (JDM) network as supporting resource. Although the JDM network is primarily a knowledge base of common sense, it contains also many specialized facts, including medicine/radiology, which have been added within the framework of IMAIOS system. With such a large lexico-semantic network, we can devise quite crude algorithms that are very efficient. In this paper, we present how semantic relations (causes, consequences, symptoms, locations, parts…) between medical terms can be extracted. For this purpose, we handcrafted some linguistic patterns from on a subset of our radiology report corpora. As semantic patterns (de (of)) may be too general or ambiguous, semantic constraints have been added. For instance, in the sentence néoplasie du sein (neoplasm of breast) the system knowing neoplasm as a disease and breast as an anatomical location, identify the relation as being a location: neoplasm r-lieu breast. An evaluation of the effect of semantic constraints is proposed.

Background

Most work concerning the extraction of semantic relation focus on domain-independent relations [START_REF] Snow | Semantic taxonomy induction from heterogenous evidence[END_REF][START_REF] Chklovski | VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations[END_REF]. In the general domain, the extraction of semantic relations between entities uses either statistical approaches [START_REF] Hindle | Noun classification from predicate-argument structures[END_REF][START_REF] Nazar | Co-occurrence graphs applied to taxonomy extraction in scientific and technical corpora[END_REF] either techniques of machine learning as well as approaches based on the use of linguistic patterns [START_REF] Hearst | Automatic acquisition of hyponyms from large text corpora[END_REF] and even approaches combining these two techniques. Seen the difficulty, according the linguistic pattern selected to determine the kind of relations between two terms (because of the ambiguity of the linguistic pattern) Girju (Girju et al., 2003) suggested adding semantic constraints in order to discover meronymy relation. They determine 20 constraint thanks to a machine learning algorithm and obtain 83% of precision. Other lexical and synctatical constraints were applied to relations expressed by verbs (Ferber ey al., 2011). Concerning relation extraction (RE) in biomedical domain, there are four main techniques. finding co-occurrence [START_REF] Jelier | Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes[END_REF], using pattern or rule [START_REF] Auger | Pattern-based approaches to semantic relation extraction: A state-of-the-art[END_REF][START_REF] Song | PKDE4J: Entity and relation extraction for public knowledge discovery[END_REF][START_REF] Rindflesch | Argument identification for arterial branching predications asserted in cardiac catheterization reports[END_REF], supervised learning-based approaches [START_REF] Song | PKDE4J: Entity and relation extraction for public knowledge discovery[END_REF][START_REF] Rink | Automatic extraction of relations between medical concepts in clinical texts[END_REF] and hybrid approach [START_REF] Suchanek | Combining linguistic and statistical analysis to extract relations from web documents[END_REF][START_REF] Chowdhury | Combining tree structures, flat features and patterns for biomedical relation extraction[END_REF]. In the medical domain, RE systems often use, as knowledge base, UMLS (Unified Medical Language System) [START_REF] Bodenreider | The unified medical language system (UMLS): integrating biomedical terminology[END_REF]. For instance, (Lee et al.) relied on UMLS to identify semantic relations between medical entities. [START_REF] Embarek | Learning Patterns for Building Resources about Semantic Relations in the Medical Domain[END_REF] proposed a system to extract only four kinds of relations (Detect, Treat, Sign and Cure) between medical entities. [START_REF] Abacha | Automatic extraction of semantic relations between medical entities: a rule based approach[END_REF] propose a method that allows extracting and annotating medical entities and relationships. They have used a rule based approach. Abacha uses a semi-automatic method for linguistic pattern generation while Lee uses a manual one. SemRep [START_REF] Srinivasan | Exploring text mining from MEDLINE[END_REF] is a tool which allows to identify the semantic relations in the biomedical texts thanks to an approach based on a set of rules [START_REF] Liu | Using SemRep to label semantic relations extracted from clinical text[END_REF]. F. [START_REF] Meng | Automating the generation of lexical patterns for processing free text in clinical documents[END_REF] propose an MSA (multiple sequence alignment) based framework for generating automatically lexical patterns (but not semantic ones). .

Our Approach: Semantic Patterns

The principle of our approach is quite simple (if not simplistic). As a first step, we identify compound terms. In a second step, we identify semantic relations between terms using semantic patterns. In the sentence fracture of orbital floor passing by the infra-orbital canal, the first step identify fracture_of_orbital_floor and infra-orbital_canal as an anatomical location. Thanks to the linguistic patterns passing by the, we are be able to validate the presence of a semantic relation between the two terms. Our technique of entity extraction can be categorized in a dictionary-based approach and our method of relation extraction uses patterns or rules. The knowledge base on which our radiological reports entity extraction relies is the French lexical network JeuxDeMots [START_REF] Lafourcade | Making people play for Lexical Acquisition with the JeuxDeMots prototype[END_REF]. Although this network is general, it contains many specialty data, including medicine/radiology, which we have added within the framework IMAIOS project. The JDM network is a lexical-semantic graph for the French language whose lexical relations are generated both through GWAP (Games with a purpose) and via a contributory tool called Diko (manual insertion and automatic inferences with validation). At the time of this writing the JDM network contains over 23 millions relations between around 500,000 terms (many with inflected forms like plurals). The identification of compound terms is made upstream compared to the content of JeuxDeMots network. We use the underscore to aggregate the two parts of a compound word so that it is considered as an entity at the time of the extraction (tibia_fracture). In our work, we are able to identify from JDM concepts like disease, symptom, anatomical location, characteristic (hyperintensity or hyperdensity), associated to terms. Moreover, to identify POS (part of speech) we use also JDM network. This information is available in the network.

Semantic Relation Extraction

Our relation extraction approach is based on the use of linguistic patterns, similar to [START_REF] Embarek | Learning Patterns for Building Resources about Semantic Relations in the Medical Domain[END_REF]. For each relation type (tableau 1), we build patterns and match them with the sentences to identify the correct relation. We have chosen these 15 semantic relations for radiology report indexation following the advice of radiologists, but they can be of any general purpose. Some authors have already noted that the use of patterns is an effective method for automatic information extraction from corpora if they are efficiently designed [START_REF] Embarek | Learning Patterns for Building Resources about Semantic Relations in the Medical Domain[END_REF][START_REF] Cimino | Automatic knowledge acquisition from MEDLINE[END_REF].

Types of relations

Linguistic Patterns + Contraint = Semantic Patterns

For many relation types of the JDM network, we designed a set of linguistic patterns (tableau 2). These patterns are (for now) manually built through partial analysis of our corpus. In our experiment, we restricted ourselves to 42 semantic patterns, 12 of which are specific to medicine.

Relations

Constraint on Patterns

For some linguistic patterns it is very difficult to determine precisely the kind of relation (for example the French connector de (of), sous (under)). So, we have added some semantic constraints on linguistic patterns. A semantic constraint is a condition that should verify the reification of one of variable of the pattern. We may have any number of constraints on $x and $y. We present some examples below:

 $x de $y If $x r_isa illness & $y r_isa anatomical_location  $x r_location $y If $x r_isa anatomical_structure and $y r_isa anatomical_structure  $x r_part_of $y  $x en $y If $x r_isa disease & $y r_isa anatomical_location $x r_location $y  $x avec $y If $x r_isa disease & $y r_isa clinical sign  $x r_symptom $y  $x sous $y If $x r_isa disease & $y r_isa treatment  $x r_against-1 (is treated by) $y  $x au niveau du $y If $x r_isa disease & $y r_isa anatomical_location $x r_location $y  $x due à $y If $x r_isa disease & $y r_isa microorganism || r_isa environment factor $x r_cause $y  $x porteur de $y If $x r_isa person & $yr_isa disease$y r_target$x  $x $y If $x r_pos Adj & $y r_pos Nom $y r_carac $x  $x $y If $x r_pos Noun & $y r_pos Adj $x r_carac $y
In some cases, it is difficult to find some proper rules.

Then we decide to incorporate some adjectival or adverbial multiword expressions in the network. For example, to deal with the noun phrase lesion in (en) hyperintensity, the expression in hyperintensity is added to the JDM network.

As mentioned above, we have crafted a small set of semantic patterns for testing purpose (we have, for the moment, identified 12 semantic patterns specific to medicine and 30 more general ones) and especially to detect properly relations for common connectors (de(of), du, en, avec, sous).

Algorithm

Starting from a given corpus (a radiological in our case), the procedure for extracting relations is informally the following:

Let S the result set, being the empty set at initialization Finding pattern occurrence in the text by moving a word window of size n (essentially similar to using a Finite State Automata implementing the recognition of the linguistic patterns) For each pattern occurrence applying constraints to the instantiated variables If constrains are verified then the associated semantic relation is associated to $x and $y, that is to say added to S Return S

The value of n is the length of the longest pattern (including both variables). The result set S is weighted, the weight is the number of time that a given semantic relations between two given terms have been found in the text. Let's take an example with the following semantic patterns:

"$x du $y" | $x r_isa disease & $y r_isa anatomical_place  $x r_location $y "$x du $y" | $x r_isa anatomical_place & $y r_isa anatomical_place  $y r_has_part $x
From the noun phrase below (in French): « contusion intra-osseuse de l'os sous-chondral en zone portante du condyle latéral » we extract: contusion intra-osseuse r_location os sous-chondral os sous-chondral r_has_part condyle latéral

Results and Discussion

In order to evaluate the performances of our algorithm, we used classical measures namely precision, recall and F-measure (table 3). From a corpus of more 30 000 medical reports, we extracted a random subset of around 120 000 relation instances for the different relation types. About 800 of these relations were manually checked for evaluating precision. For assessing recall, we manually identified the relations in about 300 medical reports and then we applied our algorithm for comparison. Globally the precision measure is quite good when we add constraints on semantic relations. The results show an improvement of the F1-measure. We notice that the method with semantic patterns improves the precision without modifying the recall. We can explain this by the fact that the addition of constraints allows a better characterization of the relation (by consequence an improvement of the precision) while the number of extracted relation does not vary (so the recall is not modified) because we do not add linguistic patterns.

A comparison with other works seems a little difficult because we extract relations from specific corpus (radiology reports). [START_REF] Embarek | Learning Patterns for Building Resources about Semantic Relations in the Medical Domain[END_REF] [START_REF] Rink | Automatic extraction of relations between medical concepts in clinical texts[END_REF][START_REF] Esuli | An enhanced CRFs-based system for information extraction from radiology reports[END_REF]. Some relations cannot be easily extracted because (not so) surprisingly they do not appear in our corpus. For instance, the relation of hypernym or synonymy is rarely present in our corpus which seems normal as radiologist knows already taxonomic information like that a carcinoma is a cancer. Another cause of limitation is the fact that some terms do not belong to the same sentence.

Our approach shows much better results with the addition of semantic constraints on relations. In our experiment, these constraints can be assessed thanks to a large knowledge which is the JDM lexical network. We also have applied our proposed method to other corpora. For a cooking corpus of 45 000 recipes, we extracted 245 000 relations with a precision of 95% (manually evaluated on a sample of 755 relations). Furthermore, we extracted 789 000 relations for randomly Wikipedia pages with a precision of 92% (manually evaluated on a sample of 1250 relations). Hypernym extraction on Wikipedia articles has a precision of about 94%.

Conclusion

In this article, we have proposed a fast method for extracting semantic relations between entities. The method is based on the use of linguistic patterns with semantic constraints to be checked with the large JDM French lexical network. We showed that adding constraints improve tremendously both recall and precision, without having to rely of a POS tagger or syntactic analyzer. Future work is to improve the coverage of our system by discovering lexical pattern from texts in an automatic way. A future work is to automate the generation of lexical pattern [START_REF] Meng | Automating the generation of lexical patterns for processing free text in clinical documents[END_REF]. We can take advantage of the knowledge between terms contained in the lexical network to infer the semantic relations hold by some recurrent linguistic patterns identified automatically by high occurrence level.

Table 1 :

 1 list of relations using for extraction

		Meaning
	r_synonym	Synonyms or quasi synonyms
	r_syn_strict	Strict synonyms (direct substitution is possible)
	r_isa	Generic (hypernym)	terms
	r_charac	Typical characteristics
	r_target	Target of disease (people, organ, etc)
	r_symptom	Symptoms of disease
	r_location	Typical locations

Table 2 :

 2 For the relation of location, we can distinguish two kinds of relations depending on the pattern. First the relation r_lieu (hepatocellular carcinoma is at the level of the liver). The second relation is holonomy. It defines the relationship between a term denoting the whole and a term denoting a member of the whole (femur r_holo lower limb). For some connectors (of in caudate lobe of liver) both relations are correct (caudate lobe r_lieu liver and caudate lobe r_holo liver).

	Note, that we also make use detection of immediate
	co-occurences of entities for characteristic relation. For
	instance mutifocal hepatocellular carcinoma (HCC)
	appears five times together, so we consider multifocal as a
	probable characteristic of HCC (HCC r_characteristic
	multifocal).

Examples of relations patterns. The actual patterns are in French.

For some relations several difficulties related to ambiguity appeared.

Table 3 :

 3 Results of the extraction of semantic relations

	without constrains
	relations	Precision Recall F-measure
	cause	90%	60%	72%
	consequence	89%	62%	73%
	location	83%	40%	54%
	treatment	88%	60%	71.3%
	part-of	75%	30%	42.9%
	target	80%	40%	53.3%
	characteristic	88%	58%	70%
	lieu	86%	40%	54.6%

Table 4 :

 4 Results of the extraction of semantic relations

	with constrains