CREATING A BILINGUAL PSYCHOLOGY LEXICON

FOR CROSS LINGUAL QUESTION ANSWERING

A Follow up Study

Andrea Andrenucci

Department of Computer and System Sciences, Stockholm University, Sweden

Keywords: Internet Services, Natural Language Interfaces, Data Mining, Cross Lingual Question Answering.

Abstract: This paper discusses a follow-up study aimed at investigating the extraction of word relations from a

medical parallel corpus in the field of Psychology. Word relations are extracted in order to create a bilingual

lexicon for cross lingual question answering between Swedish and English on a medical portal. Six different

variants of the corpus were utilized: word inflections with and without POS tagging, syntactically parsed

word inflections, lemmas with and without POS tagging, syntactically parsed lemmas. The purpose of the

study was to analyze the quality of the word relations obtained from the different versions of the corpus and

to understand which version of the corpus was more suitable for extracting a bilingual lexicon in the field of

psychology. The word alignments were evaluated with the help of reference data (gold standard) and with

measures such as precision and recall.

1 INTRODUCTION

Users of medical portals in general, regardless of

their background, value the possibility of

formulating their information needs in their own

native language. In Question Answering this is

possible with the help of Machine Translation (MT),

which converts user questions into the language of

the texts from where the answers are extracted. This

paradigm is called Cross Language Question

Answering, CLQA (Aunino, Kuuskoski and

Makkonen, 2004).

The Web4health medical portal

(http://web4health.info) supports cross language

question answering (CLQA). User questions are

translated into English with the help of Systran’s

MT system (http://www.systransoft.com) and are

then used to retrieve answers from the knowledge

base of the portal. One problem with the existing

implementation is that Systran implements medical

lexicons which are not tailored to the specific

domain of the portal, i.e. psychology and

psychotherapy. The aim of this research is to

produce a bilingual lexicon for Swedish and English

that overcomes this gap. For this purpose we have

investigated the possibility of automatically

extracting word relations from a parallel corpus

(Swedish and English), which consists of

Web4health’s knowledge base. The corpus was

extracted in two versions, one version consisting of

words in their inflected forms and another version

consisting of word lemmas. For both versions we

also provided three variants: 1) a variant annotated

with part of speech (POS) tagging, 2) a variant with

POS-tagging and syntactically parsed 3) a “as-is”

variant (i.e. without POS tagging nor parsing). The

purpose of the study was to analyze the quality of

the word relations obtained from the different

versions of the corpus and the quality of the relations

with different word frequencies. This was done in

order to understand which version of the corpus was

more suitable for extracting a bilingual lexicon.

The texts were aligned at the paragraph, sentence

and word level with the Uplug toolkit (see section

3), a collection of tools for processing parallel

corpora, developed by Jörg Tiedemann (2003a).

Uplug utilizes both statistical and linguistic

information in the alignment process. The

alignments were evaluated at the word level with the

help of reference data (gold standard), which were

constructed before the word alignment process. The

gold standard was created with a frequency based

sampling approach (see section 4.2).

The paper is structured as follows: section two

describes related research in the field of cross

lingual question answering within medicine. Section

Andrenucci A.

CREATING A BILINGUAL PSYCHOLOGY LEXICON FOR CROSS LINGUAL QUESTION ANSWERING - A Follow up Study.

DOI: 10.5220/0002803400950102

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

three summarizes the knowledge base and the Uplug

toolkit. Section four and five describe the pilot study

and its quantitative results. The paper is concluded

with a discussion of the results (section six) and the

paper conclusions (section seven).

2 RELATED RESEARCH

Several projects have focused on developing lexical

resources for the medical domain. Marko et al.

(2006) created multilingual medical lexicons

mapping monolingual lexicons (in French, English

and German) to one another. No parallel corpora

were utilized. The researchers mapped terms in each

monolingual lexicon to interlingua representation

(i.e. a morphosemantic representation) of the terms.

This methodology is similar to pivot alignment

(Borin 99) and differs from the approach of this

research that utilizes parallel corpora without

mapping terms to intermediate representations.

Nyström et al. (2006) developed a medical

English-Swedish dictionary utilizing word alignment

of several international medical terminology

resources such as MeSH (Medical Subject

Headings) and ICF (International Classification of

Functioning, Disability And Health). There are two

major differences between Nyströms work and our

research. Nyström produced a dictionary with

medical terms for medical practitioners; our purpose

is to produce a lay dictionary (consumer vocabulary)

of terms that are understandable for common people.

Nyström also utilized metrics of precision and

recall but only considered entirely correct

alignments. This approach works well when it

comes to evaluating single word alignments

(SWUs), but is too coarse for the evaluation of Multi

Word Units (MWUs), which often imply partially

correct results (Tiedemann, 2003b, p. 26). Since we

are even interested in semi automatically extracting

MWUs we even considered partially correct links

(links that have at least one correct word on source

and target side).

Baud et al. (1998) also built a bilingual medical

lexicon aligning the French and English

International Statistical Classification of Diseases

and Related Health Problems, Tenth Revision (ICD-

10). Unlike this research, no syntactic parsing was

performed and no recall results were provided.

3 THE KNOWLEDGE BASE

AND THE UPLUG TOOLKIT

The Web4health medical portal (http://

web4health.info) is well established among the

medical portals on the Web. Psychiatrists and

psychotherapists from five different European

countries (Italy, Sweden, Holland, Greece and

Germany) use the portal to jointly develop a set of

semantically classified Web pages that answer

questions in matters of psychological and

psychotherapeutic advice. Users consult the

knowledge base submitting questions in natural

language, which are then matched against pre-stored

FAQ-files (Frequently Asked Questions).

The Uplug toolkit (Tiedemann, 2003a) is a

collection of tools for processing parallel corpora. Its

main functionality consists of sentence and word

alignments of bilingual texts. The main idea behind

Uplug’s alignment process is to utilize both

linguistic and statistical information in order to

extract word relations. Each individual piece of

information is called a clue,

),( tsC

, and is defined

as a probability that indicates an association between

two sets of words s and t in parallel texts. Formally

it is defined as a weighted association A between s

and t, where w

is used to weight and normalize the

score of A

),()(),( tsAwaPtsC

iiii

(1)

All clues are then combined in an overall measure,

which is defined as the disjunction of all indications:

)...()(),(

21 nallall

aaaPaPtsC ∪∪∪

(2)

Clues are not mutually exclusive. The addition rule

for probabilities generates the following formula for

a disjunction of two clues:

)()()()(

212121

aaPaPaPaaP ∩−

∪

(3)

Two main types of clues are considered: basic

(static) clues, whose value is constant for a pair of

lexical items and dynamic clues, whose values are

learned dynamically during the alignment process.

Basic clues include co-occurrence coefficients (the

Dice coefficient, Tiedemann 1999), string similarity

coefficients (the longest common subsequence ratio,

Melamed 1995) and GIZA++ clues (Och and Ney,

2003), based on IBM models (Brown et al., 1993)

and Hidden Markov Model. Dynamic clues include

patterns of POS labels, phrase types and word

positions. The system aligns first sentences and

words with the basic clues and then utilizes the

aligned links as training data in order to learn

new

dynamic clues and improve the quality of the

alignments. For instance, examining POS tags in

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

source and target language, it is possible to estimate

the probabilities of translation relations between

words that belong to certain word classes.

A huge advantage of the Uplug tool is that it

supports the dynamic construction of alignments

with multi word units (MWUs), i.e. noun phrases,

idiomatic expressions and other phrasal

constructions that should not be split up in the

alignment process (Tiedemann, 2003b, p. 18).

4 IMPLEMENTATION OF THE

STUDY

This section outlines how the study was conducted.

It describes how the corpora were annotated and

prepared for the alignment process (section 4.1),

plus how the results were extracted and evaluated

(section 4.2).

4.1 The Corpus Selection and

Annotation

The parallel corpus of this study consists of the

knowledge base of the Web4health portal, i.e. FAQs,

or question/answer pairs, in the source language

(Swedish) and the target language (English). The

Swedish corpus consists of circa 135301 tokens and

the English counterpart of circa 143118 tokens.

Prior to utilizing the randomly chosen texts, we

scanned and proofread the material and, when

necessary, corrected it to ensure its completeness

and correctness. This was a difficult and time

consuming task, since the documents in the

repository are often translated freely and the

structure of the texts tend also to differ, with

sentences or phrases that are available in one

language only.

Prior to starting the alignment process, some

preliminary work was needed in order to prepare the

corpora. Since the FAQ documents are annotated

with HTML tags, the texts had first to be cleaned up

by the existing tags and then converted into plain

text. The Uplug toolkit was then used for encoding

the texts with ISO88591 for Latin1 (which includes

Swedish and English) and annotating them with

XML Corpus Encoding Standard (XCES) (Ide and

Priest-Dorman, 2000). Sentence splitting and

tokenization were included in this step.

A version of the bilingual corpus was

lemmatized with the CST Lemmatizer (Jongejan and

Haltrup, 2005) which is a trainable, rule-based tool

that works with languages that utilize inflectional

suffixes, such as Swedish and English.

The Trigrams’n Tags tagger (Brants, 2000) was

utilized to annotate the POS-tagged versions of the

corpora. TnT was chosen since it is the tagger that

has the highest overall accuracy among data-driven

taggers and succeeded best in the annotation of both

known and unknown words in Swedish (Megyesi,

2000).

The tagger was trained on Swedish (Megyesi

2002) using the StockholmUmeå Corpus (SUC,

1997), and utilized for the labels the PAROLE

annotation scheme (Ejerhed and Ridings, 1995), a

tagset that include part-of-speech and morphological

features such as gender and number of the words.

The Penn Treebank corpus and its tagset (Marcus,

Santorini och Marcinkiewicz, 1993), which also

encodes morphological information such as number,

were utilized for the English language.

The English part of the bitext was parsed with

GROK (Baldrige J.), an open source library for

Natural Language Processing. The Swedish part was

parsed with a context free grammar parser for

Swedish (Megyesi 02).

4.2 Evaluation Method and the Gold

Standards

After aligning the different versions of the corpus at

the sentence level, capital letters were converted to

non-capital letters in order to improve precision of

the word-level alignment. Once the word alignment

was finished, a table, with word-pair frequencies

sorted in descending order, was constructed for each

corpus version in order to see which alignments

occurred more often. These frequency tables were

later utilized for analyzing the evaluation results (see

section 5 and 6). Two main evaluation techniques

are utilized when it comes to evaluating word

alignment (Ahrenberg et al., 2000): automatic

evaluation with a reference alignment (Gold

Standard) or manual evaluation by experts.

Automatic evaluation was preferred since reference

alignments can be re-utilized and it is possible to

control the process of selecting the reference data,

focusing for instance on certain word types or words

from certain frequency ranges (Merkel, 1999).

Our reference data (or gold standard) were

aligned manually according to detailed guidelines

(Merkel, 1999). They were compiled by randomly

selecting word samples from the parallel corpus. The

word samples were limited to content units (phrases

and content words, i.e. words with a full meaning of

their own). We applied a frequency balanced

approach, i.e. we grouped entries according to the

following frequency ranges: 40 entries with

frequency above 10, 40 entries with frequency 7-9,

CREATING A BILINGUAL PSYCHOLOGY LEXICON FOR CROSS LINGUAL QUESTION ANSWERING - A Follow

up Study

40 with frequency 5-6, 40 with frequency 3-4 and 40

with frequency 1-2.

The GS included links of type “regular” (standard),

“fuzzy” (somehow semantically overlapping but

with different POS or different degrees of

specification) and “null” (omissions). Complex

MWU links were also included.

As stated of Ahrenberg et al. (2000), word

alignment can be viewed as a retrieval problem. For

this reason, when evaluating the quality of the

alignments, it is appropriate to apply measures from

the field of information retrieval such as precision

and recall. By precision it is meant the ratio of

correctly aligned items in proportion to the number

of aligned items and by recall the ratio of correctly

aligned items in proportion to the total number of

correct items (reference data). However a problem

of these measures is that they do not handle

partially

correct links

, i.e. links that have at least one correct

word on source and target side, since links are either

considered as entirely right or entirely wrong. This

approach works well when it comes to evaluating

single word alignments, but is too coarse for the

evaluation of MWUs, which often imply partially

correct results (Tiedemann, 2003b, p. 26).

In order to overcome this deficiency we chose to

apply refined metrics of precision and recall

(Tiedemann, 2003b, p. 68) that measure the

degree

of correctness

of the proposed links. They calculate

a partiality value Q that is proportional to the

number of words that are in common between the

proposed alignments and the reference data:

src

aligned is the set of source language words and

trg

aligned the set of target language words in link

proposals for a reference link x in the GS.

src

correct and

trg

correct define the sets of source and

target words of reference link x. Precision (P) and

recall (R) are then defined with the help of Q:

aligned is the total number of correct, incorrect and

partially correct links in relation to the GS and

correct

represents the size of the GS.

These metrics handle also partially correct links

in a more fine grained way, unlike other coarser

approaches (e.g. the PLUG metrics, Ahrenberg et

al., 2000) that penalize partially correct links with a

constant value without considering the degree of

correctness of the links.

5 QUANTITATIVE RESULTS

Our study produced the quantitative results that are

shown in table 1 below. In the next section we

discuss the results with the help of the data from the

frequency tables and elicit the differences between

each version of the corpora.

The results in table 1 present some interesting

differences between the corpora. For what concern

word inflections, the POS tagged corpus and the

shallow parsed corpus had better precision/recall for

low frequency MWUs and SWUs (frequency rate 1-

2, 3-4). With higher frequency rates “untagged”

word inflections achieved slightly better results than

POS tagged and parsed alignments.

Alignments based on lemmas had in general worse

statistical results than word inflections. This is a

surprising effect that partly contradicts the results of

our pilot study that bootstrapped this research

(Andrenucci 2007). However the pilot study utilized

a corpus that was only a small subset of the whole

parallel corpus: the Swedish sample corpus

consisted of 12800 tokens and the English

counterpart of circa 13000.

Low frequency (1-2) lemmas achieved worse

precision results than POS tagged and syntactically

parsed lemmas.

With higher frequencies (5-6, 7-9, >10) the “as-is”

lemmas gathered better precision results for both

SWUs and MWUs. Syntactically parsed lemmas had

lower precision results than POS tagged lemmas in

all frequency rates.

6 DISCUSSION AND ANALYSIS

As a complement to the statistical data presented in

table 1 we analyzed the frequency tables extracted

from the alignments and compared the results, trying

to elicit the similarities and the differences among

the different versions of the corpus. We discuss our

analysis with the help of some examples of the word

relations that are presented in tables 2 and 3.

∑

recall

correct

∑

precision

aligned

trg

src

trg

src

recall

correctcorrect

correctalignedcorrectaligned

∩+∩

trg

src

trg

src

precision

alignedaligned

correctalignedcorrectaligned

∩+∩

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

Table 1: Precision and Recall results.

Frequency

rate

Word inflections

Precision

Word inflections

Recall

POS Words

Precision

POS Words

Recall

Parsed Words

Precision

Parsed Words

Recall

MWUs 1-2 52,55 64,39 63,38 84,75 65,62 84,94

MWUs 3-4 73,71 88,81 74,10 90,17 76,27 90,5

MWUs 5-6 71,39 86,34 70,60 86,27 69,52 86,25

MWUs 7-9 67,50 85,59 64,31 83,59 63,9 83,46

MWUs >10 67,35 85,19 63,90 83,06 61,24 82,83

SWUs 1-2

73,15 87,37 76,30 90,40 77,69 90,40

SWUs 3-4 82,01 89,91 88,02 99,07 88,39 99,23

SWUs 5-6 86,14 96,49 81,75 93,50 81,91 93,57

SWUs 7-9 86,90 96,30 84,12 95,12 84,17 95,07

SWUs >10 87,66 98,08 85,83 97,36 79,70 94,57

Frequency

Rate

Lemmas

Precision

Lemmas Recall POS Lemmas

Precision

POS Lemmas

Recall

Parsed Lemmas

Precision

Parsed Lemmas

Recall

MWUs 1-2 47,51 68,02 58,12 74,02 56,52 72,79

MWUs 3-4 42,01 65,68 43,23 66,88 41,52 63,60

MWUs 5-6 53,29 66,93 40,35 56,83 39,36 52,09

MWUs 7-9 56,30 78,01 53,03 74,61 51,56 73,71

MWUs >10 54,72 72,30 52,36 71,28 51,99 70,31

SWUs 1-2

50,20 59,85 52,73 62,88 51,21 62,12

SWUs 3-4 60,52 74,44 55,31 72,22 54,64 72,22

SWUs 5-6 59,33 72,97 56,54 71,17 52,55 70,97

SWUs 7-9 58,65 72,81 57,54 70.18 53,93 67,54

SWUs >10 55,67 72,44 53,86 67,95 50,76 6,51

Lemmas with POS Tags VS Lemmas without

POS/Parse Tags.

The statistical results for Lemmas

with and without POS/Parsing and the examination

of the frequency tables clarified some points of

difference. POS- tagged lemmas were more precise

when aligning compound words (which were

included among the MWUs) with low frequency rate

(1 or 2).

Low frequency POS tagged and syntactically parsed

MWUs had fewer additions, i.e. words that are

occurring in the alignments but that are not present

in the reference links (e.g. “nedsatt minneskapacitet

- memory deficit” VS “nedsatt minneskapacitet -

memory deficit failure” see table 2), and less

incorrect links.

The POS tagging and syntactic parsing proved to be

useful in aligning words consisting of dissimilar

strings and with low co-occurrence frequency, but

sharing the same POS (e.g. two nouns: “matstrupe -

oesophagus” 1-2 VS an adjective and a noun:

“överkänslig - oesophagus”).

Alignments consisting of SWUs achieved fewer

addictions in the corpus without POS nor parsing,

particularly with higher frequency rate (“fördom -

prejudice” >10, “alkoholist - alcoholic” >10, “toalett

- toilet” 9-7, “tanke- thought” 9-7, see table 2). POS

tagged lemmas produced slightly better results than

syntactically parsed lemmas in all frequency rates.

Inflected Words with POS, Syntactic Parsing VS

Inflected Words.

As shown in table 1, alignments

of inflected words with POS and syntactic parsing

obtained, in comparison to inflected words without

POS, better precision, recall results as well as a

higher number of correct links in lower frequency

rates (1-2 and 3-4) for both MWUs and SWUs. The

morphological information helped to disambiguate

the gender of Swedish adjectives in noun phrases,

including them in the alignment when they agreed

with the head noun and their inclusion was

necessary to build a conceptual unit (“dåligt

uppförande - misbehaviour” VS “uppförande -

misbehaviour” 1-2, where “dåligt” means “bad” and

“uppförande” means “behaviour”, see table 3). It

also helped to link nouns with the same number

(“barndomsupplevelser - childhood experiences” VS

“barndomsupplevelser - childhood”). For what

concerns single word units the morphological

information was helpful for aligning words sharing

the same definiteness (“förmågan - the ability” VS

“förmågan - ability”) or POS (e.g. two adjectives:

“felaktiga - inappropriate” instead of a noun and an

adjective “antaganden - inappropriate”).

CREATING A BILINGUAL PSYCHOLOGY LEXICON FOR CROSS LINGUAL QUESTION ANSWERING - A Follow

up Study

Table 2: Alignment examples of lemmas with and without

POS/Parsing.

Lemmas no

POS

Lemmas with

POS

Parsed

Lemmas

Frequency >10

alkoholist –

alcoholic

alkoholist -

alcoholic drinker

abuse

alkoholist -

alcoholic drinker

abuse

Fördom –

prejudice

Fördom

närstående -

prejudice

relative

Fördom

närstående -

prejudice

relative

Frequency 7-9

toalett - toilet toalett – fear

toilet

toalett -

fear toilet

tanke – thought tanke – effect

thought use

tanke – effect

thought use

Frequency 1-2

överkänslig -

oesophagus

matstrupe –

oesophagus

matstrupe –

oesophagus

nedsatt

minneskapacitet

- memory deficit

failure

nedsatt

minneskapacitet

- memory deficit

nedsatt

minneskapacitet

– memory

deficit

problem humör

svängning -

problem mood

swing

humörsvängning

- mood swing

humörsvängning

- mood swing

The POS based and parsed word relations had also

better alignments among phrasal verbs that consisted

of a verb and a particle in Swedish and a verb in

English (“tänka ut - decide” VS “tänka - decide”;

“klara av -handle” VS “klara - handle”). They even

provided better alignments of verbs in passive forms

(“omvandlas – be converted” VS “omvandlas intas

med i det här - converted enters through into”).

It is interesting to point out that, for what

concerns “as-is” word inflections, it was more

difficult for low frequency words with poor string

similarity coefficients to get precise alignments. The

system tended to attach extra words to equalize the

different string lengths. The information contained

in POS and syntactic analysis overcame this problem

though (ex: svårighetsgrad – reflect severity VS

svårighetsgrad - severity).

The statistical figures of “as-is” word inflections

are higher than POS—tagged and shallow parsed

word inflections for frequency rates 5-6, 7-9 and >10

(see table 1). With higher frequency rates the higher

co-occurrence coefficient values (the Dice

coefficient, see chapter 3) compensated the lack of

morphological information in “as-is” word

inflections (e.g. tvångstankar - obsessive thoughts

Table 3: Alignment examples of inflected words with and

without POS/Parse.

Word

Inflections

Word Inflections

with POS

Parsed

Word

Inflections

Frequency 1-2

uppförande –

missbehaviour

dåligt uppförande -

missbehaviour

dåligt

uppförande

missbehavi

our

svårighetsgrad -

reflect severity

svårighetsgrad –

severity

svårighetsg

rad –

severity

Frequency 3-4

förmågan -

ability

förmågan – the

ability

förmågan -

the ability

magkatarr -

organs gastritis

magkatarr -

gastritis

magkatarr

– gastritis

Frequency 5-6

hetsätare -

compulsive eater

hetsätare bli frisk -

eater compulsive

eater

hetsätare

bli frisk -

eater

compulsive

eater

Frequency 7-9

alkoholproblem -

alcohol problems

människor

alkoholproblem

motstånd - people

alcohol problems

attempts

Människor

alkoholprobl

motstånd -

people

alcohol

problems

attempts

Frequency >10

tvångstankar -

obsessive

thoughts

tvångstankar

ögonsmärtor -

obsessive thoughts

tvångstank

ögonsmärto

r -

obsessive

thoughts

vanföreställninga

r –delusions

brukarna

vanföreställningarn

a -delusions

brukarna

vanföreställ

ningarna –

delusions

VS tvångstankar ögonsmärtor - obsessive thoughts,

see table 3) and the role played by POS and shallow

parsing was also less important in the tagged

corpora. The broader the amount of dynamic clues

the lesser was the quality of the alignments (within

the same frequency rate) for high frequency rates.

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

100

Inflected Words with POS and Syntactic Parsing

Tags VS Lemmas with POS and Syntactic

Parsing.

As we stated earlier, alignments based on

lemmas had in general worse statistical results than

word inflections. The truncation caused by the

lemmatization process influenced the alignment at

the paragraph and sentence level, worsening in some

cases even the quality of sentence alignments (since

the system utilize a length-bases sentence alignment

algorithm, Church 93). Lemmatization influenced

also the alignment at the word level. The removal of

number, definiteness and gender information in

Swedish nouns and adjectives, obtained through

lemmatization, determined a coarser POS tagging,

affecting the dynamic clues and worsening in

particular the quality of the produced alignments.

For instance removing the gender suffix in

adjectives made it more difficult to individuate the

nouns the adjectives referred to, causing less precise

alignments in comparison to inflected forms (e.g.

“uppförande - misbehaviour” instead of “dåligt

uppförande - misbehaviour”, where “dåligt” means

bad and “uppförande” means behaviour).

Errors deriving form the lemmatization caused

erroneous POS tagging (for instance removing the

suffix “t” from Swedish adverb “vanligt”, as if they

were adjectives referring to “ett-”words, made the

tagger mark those words as adjectives instead of

adverbs). This also affected dynamic clues such as

word positions and POS-patterns, and thus

influenced the quality of the alignments.

Alignments of proper nouns such as medicine names

with similar names in both languages (Concerta -

Concerta, Trifluoperazin - Trifluoperazine), were

generally correct both for lemmas and word

inflections in all frequency rates, since those nouns

were not truncated by lemmatization and their string

similarity/length coefficients were very high .

7 CONCLUSIONS

This paper has examined the extraction of word

relations from a medical parallel corpus in order to

create a bilingual lexicon for cross lingual question

answering between Swedish and English. Six

different variants of the sample corpus were created:

word inflections, word inflections with POS tagging,

word inflections with syntactic parsing, lemmas,

lemmas with POS tagging, lemmas with syntactic

parsing. The results of this follow-up study partly

confirm the results of our pilot study. POS tagging

and shallow parsing enhances the quality of the

alignments of both SWUs and MWUs, particularly

for units with low frequency (1-2, 3-4). The role of

morphosyntactic information was particularly

important when aligning dissimilar strings sharing

the same POS or phrase (both SWUs and MWUs).

The information about gender, number and

definiteness contained in the suffixes of word

inflections was particularly crucial for the quality of

alignment of low frequency MWUs. Considering

that the medical domain is characterized by multi-

word terms that are either unknown to generic

lexicons or have meanings specific to this domain

(Rinaldi et al, 2004), it is advisable to utilize corpora

with syntactically parsed inflections as source for the

extraction of bilingual lexicons for extracting low-

frequency MWUs. If the target consists of words

with higher frequency rates it is advisable to utilize

“as is” word inflections.

Unlike our pilot study the results of the

lemmatized alignments were much lower than word

inflections. It is thus not advisable to lemmatize the

corpora prior to extracting the corpora, since the

lemmatization process creates problems at the

sentence, paragraph and word level, influencing

negatively the quality of the extracted alignments (at

least with the tools that we utilized). Lemmatization

should be applied after extracting the word

alignments, in order to group together words sharing

the same base form in the source language or target

language and facilitate the extraction of synonym

lists in both languages.

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